Method and System for Finding a Solution to a Provided Problem Using Family Tree Based Priors in Bayesian Calculations in Evolution Based Optimization

ABSTRACT

A method for finding a best solution to a problem is provided. The method includes evolving candidate individuals in a candidate pool by testing each candidate individual of the candidate individuals to obtain test results, assigning a performance measure to each of the tested candidate individuals in dependence upon the test results, discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and adding, to the candidate pool, a new candidate individual procreated from parent candidate individuals remaining in the candidate pool, and repeating the evolution steps to evolve the candidate individuals in the candidate pool. The method further includes selecting, as a winning candidate individual, a candidate individual from the candidate pool having a best probability to beat a predetermined score, the probability to beat the predetermined score being determined in dependence upon a Bayesian posterior probability distribution of a particular candidate individual.

CROSS REFERENCE TO OTHER APPLICATIONS

This application claims priority to U.S. Provisional Application No. 62/719,452, titled “METHOD AND SYSTEM FOR FINDING A SOLUTION TO A PROVIDED PROBLEM USING FAMILY TREE BASED PRIORS IN BAYESIAN CALCULATIONS IN EVOLUTION BASED OPTIMIZATION”, filed 17 Aug. 2018 (Attorney Docket No GNFN 3332-1). The priority application is incorporated by reference for all purposes.

This application incorporates by reference U.S. Nonprovisional Patent Application No. 15/911,048, entitled “A METHOD AND SYSTEM FOR FINDING A SOLUTION TO A PROVIDED PROBLEM BY SELECTING A WINNER IN EVOLUTIONARY OPTIMIZATION OF A GENETIC ALGORITHM,” filed on 2 Mar. 2018 (Attorney Docket No. GNFN 3309-2), for all purposes.

FIELD OF THE TECHNOLOGY DISCLOSED

The technology disclosed relates generally to genetic algorithm evolution, data mining and finding a solution to a provided problem by selecting a winning candidate using genetic algorithms, and in particular relates to web interface generation, testing and evolution using family tree based priors in Bayesian calculations for evolving web interfaces.

BACKGROUND

The subject matter discussed in this section should not be assumed to be prior art merely as a result of its mention in this section. Similarly, a problem mentioned in this section or associated with the subject matter provided as background should not be assumed to have been previously recognized in the prior art. The subject matter in this section merely represents different approaches, which in and of themselves can also correspond to implementations of the claimed technology.

A goal of evolving genetic algorithms and data mining is usually to solve some type of a problem. This can be accomplished by identifying a winner, i.e. the best candidate found during the evolution of many candidate solutions. The winner (i.e., the best candidate) is then employed in an application and good performance is expected to continue. For instance, when evolving web interfaces (e.g., webpages) that convert casual browsers (customers) to paying customers, the result is a web interface (webpage) that converts at an expected rate. However, the estimated or expected behavior of the webpages is often times based on a lucky response of a webpage.

For example, the performance of candidates can be measured during evolution of the genetic representation of a candidate through sampling. Sampling is performed by showing a candidate a relatively small number of examples, or subjecting the candidate to a small number of simulated situations, to see how well it performs. As a candidate, a web interface can be tested on 1,000 users. The performance of the web interface converting the 1,000 users to paying customers is an average of these samples, i.e. a statistical estimate. This small sample size and/or small number of simulated situations can lead to inaccurate results.

Further, web interface optimization uses A/B testing or multivariate analysis, where only a small number of candidate webpages are tested. A/B testing comprises designing two different versions of the same webpage (i.e., version A and version B), showing the different versions to different users, and collecting statistics on how well each version of the webpage was able to convert browsing customers to buying customers. This process allows incorporating human knowledge about the domain and conversion optimization into the design, and then testing the effect of the optimization. After observing the results, new designs can be compared and gradually improved. This A/B testing process is difficult and time-consuming and, as a result, only a very small fraction of webpage designs can be tested in this way. Therefore, subtle interactions in the design may be missed completely.

Multivariate analysis tests various combinations of a small number of variables. This approach relies on human intuition regarding which candidates of a genetic algorithm are worth testing, and therefore many good candidates (e.g., web interfaces) may never be found or implemented.

Further, common methods for running controlled experiments on websites include sophisticated conversion optimization solutions. Conversion optimization includes testing multiple combinations and variations of webpages and page elements at the same time. For example, two alternative images, plus two alternative headlines, plus two copy text alternatives, for a total of twenty-seven possible combinations (including the original control versions) may be provided. Thus, conversion optimization introduces a rather complex set of permutations and combinations that need to be analyzed to determine the most effective combination of page elements that truly engage the users.

These above-described shortcomings for webpage optimization become more prevalent as big data plays a more important role in web personalization. For example, the number of data signals, the complexity of rules and the sheer number of outcomes has increased exponentially, which amplifies these shortcomings. As that happens, human optimization simply cannot be done except perhaps after the fact, where there is little to no opportunity to impact the outcome. Algorithmic optimization is required. However, simple linear regression algorithms that can handle linear relationships and correlations may not be able to sufficiently create improved outcomes, given the vast number of data inputs and resulting measurements that have to be processed to predict performance.

Typically, to address these issues more user traffic is directed to the best candidates, with no guarantees or bounds, while knowing that resulting estimates are likely to be overestimates. This problem is a severe instance of the multiple hypothesis problem in statistics. The standard solution to this problem is to perform Bonferroni correction. Bonferroni correction essentially reduces the confidence by a factor of 1/N if N candidates are being tested. Given the large number of candidates, Bonferroni correction is not helpful in evolutionary optimization of genetic algorithms to identify one or more winning candidates. A better solution is described below.

SUMMARY OF THE INVENTION

The technology disclosed provides a so-called machine learned conversion optimization (MLCO) system that uses evolutionary computations to solve a provided problem by efficiently identifying most successful (i.e., a “winner”) evolutions of genetic algorithms using a scoring system, such as fitness scores, neighborhood fitness scores and Bayesian calculations. This will identify the best candidates for further evolution or implementation.

The technology improves on previous methods for selecting winners in evolution-based optimization in that selection of a winner or winners and involves using Bayesian calculations along with (or without) a candidate's own fitness estimate to select the winner(s) and/or to report its performance.

For selection of a winner or winners, Bayesian calculations can be implemented to address issues such as a peeking problem of declaring statistically significant candidates incorrectly by checking data too frequently and early. A Bayesian implementation involves the calculation of a prior (e.g., a prior probability distribution). However, it is difficult to construct an accurate prior probability distribution that predicts the behavior of tested candidates. Many methodologies for creating a prior probability distribution just make uneducated guesses. This uneducated guess approach at prior probability distribution creation can lead to inaccurate predictions. This is insufficient, especially in the case of evolution-based optimizations, where there is a better alternative available.

In particular, in certain approaches to evolutionary optimization, every candidate produced for testing is a combination of variations from at least two “parent” candidates in the previous generations. Each of these candidates has been tested with real data. In the website conversion optimization environment, each of the candidates has been tested with real traffic against a control set of visitors seeing an unaltered control version. Each of the parent candidates has the same set of information of candidates that went into creating them. This means the system already has substantial data on the performance of the ‘genetic material’ that went into constructing a candidate. An embodiment of the invention can use the performance of this ‘Family Tree’ of candidates (e.g., parent candidates) to calculate an accurate prior probability from recent historical information (e.g., from Bayesian posterior probability distributions of the parent candidates). In other words, past “posterior” performance of “parent” candidates can be used to calculate the prior probability distribution for a “child” candidate when determining the posterior probability distribution that the “child” candidate will provide a desired result.

This is a direct mechanism to use previous testing of candidates to improve the accuracy of predictions of ‘probability to beat control,’ ‘probability to beat all’ and the ‘expected range of performances’ (e.g. candidate's conversion rate). It improves, as compared to other prior methodologies, as more traffic is driven to it because, among other things: (i) the performance of the most recent candidates, created from just high performing candidates (e.g., configurations or ideas) begins to further outperform the average candidate, making existing prior assumptions more inaccurate; (ii) as candidates become more differentiated from each other, it becomes less and less appropriate to use the same prior across all candidates, and (iii) based on the number of visitors to a webpage, conversions and the prior, it is possible to come up with a distribution of what is believed to be the candidate conversion rate. This “parent-based prior” approach allows for finding a more realistic distribution and a better estimation of the conversion rate.

In an MLCO embodiment, the system can use the evolutionary computations to efficiently identify most successful webpage designs (configurations) in a search space without testing all possible webpage designs in the search space. The search space can be defined based on webpage designs provided by marketers. Website funnels (e.g., a single webpage or multiple webpages) are represented as genomes. Genomes identify different dimensions and dimension values of the funnels. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes (genetic algorithms) that are likely to perform well. Each candidate is tested only to the extent that it is possible to decide whether it is promising, i.e., whether it should serve as a parent for the next generation of genetic algorithms or should be discarded.

According to an embodiment of the present technology, computer-implemented method for finding a best solution to a provided problem by selecting one or more winning candidate individuals is provided. The computer-implemented method includes storing, in a memory, a population of candidate individuals in a candidate pool, and evolving the candidate individuals in the candidate pool by performing evolution steps including: testing each candidate individual of the candidate individuals to obtain test results, assigning a performance measure to each of the tested candidate individuals in dependence upon the test results, discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and adding, to the candidate pool, a new candidate individual procreated from parent candidate individuals remaining in the candidate pool after the discarding of the candidate individuals. The computer-implemented method further includes repeating the evolution steps to evolve the candidate individuals in the candidate pool, and selecting, as a winning candidate individual, a candidate individual from the candidate pool having a best probability to beat a predetermined score, the probability to beat the predetermined score being determined in dependence upon a Bayesian posterior probability distribution of a particular candidate individual. These “winners” then have a high likelihood of being good solution(s) to the provided problem.

According to another embodiment of the present technology, a non-transitory computer-readable recording medium having instructions recorded thereon for finding a best solution to a provided problem by selecting one or more winning candidate individuals is provided. The instructions, when executed by a processor of a computer, cause the computer to execute a method. The method includes storing, in a memory, a population of candidate individuals in a candidate pool, and evolving the candidate individuals in the candidate pool by performing evolution steps including: testing each candidate individual of the candidate individuals to obtain test results, assigning a performance measure to each of the tested candidate individuals in dependence upon the test results, discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and adding, to the candidate pool, a new candidate individual procreated from parent candidate individuals remaining in the candidate pool after the discarding of the candidate individuals. The computer-implemented method further includes repeating the evolution steps to evolve the candidate individuals in the candidate pool, and selecting, as a winning candidate individual, a candidate individual from the candidate pool having a best probability to beat a predetermined score, the probability to beat the predetermined score being determined in dependence upon a Bayesian posterior probability distribution of a particular candidate individual. These “winners” then have a high likelihood of being good solution(s) to the provided problem.

In a further embodiment of the present technology, a computer-implemented system for finding a best solution to a provided problem by selecting one or more winning candidate individuals is provided. The computer-implemented system includes a memory storing a population of candidate individuals in a candidate pool and a conversion system evolving the candidate individuals in the candidate pool by performing evolution steps including: testing each candidate individual of the candidate individuals to obtain test results, assigning a performance measure to each of the tested candidate individuals in dependence upon the test results, discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and adding, to the candidate pool, a new candidate individual procreated from candidate individuals remaining in the candidate pool after the discarding of the candidate individuals. The conversion system also repeats the evolution steps to evolve the candidate individuals in the candidate pool. The computer-implemented system further includes a winner selector which selects, as a winning candidate individual, a candidate individual from the candidate pool having a best probability to beat a predetermined score, the probability to beat the predetermined score being determined in dependence upon a Bayesian posterior probability distribution of a particular candidate individual. These “winners” then have a high likelihood of being good solution(s) to the provided problem.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. The color drawings also may be available in PAIR via the Supplemental Content tab.

In the drawings, like reference characters generally refer to like parts throughout the different views. Also, the drawings are not necessarily to scale, with an emphasis instead generally being placed upon illustrating the principles of the technology disclosed. In the following description, various implementations of the technology disclosed are described with reference to the following drawings, in which:

FIG. 1 depicts a conversion system that implements evolutionary computations to identify high performing candidate individuals in a candidate search space.

FIG. 2 illustrates modules that can be used to implement the evolutionary computations of the conversion system in FIG. 1.

FIG. 3 shows a workflow of the disclosed machine learned conversion optimization (MLCO).

FIG. 4 illustrates a funnel with multiple web interfaces.

FIG. 5 depicts a web interface layout of a funnel.

FIG. 6 shows a web interface of a funnel with its dimensions and dimension values.

FIG. 7 is a symbolic drawing of one implementation of transforming an editor design representation of a starter funnel into a runtime design representation.

FIG. 8 depicts a symbolic drawing of one implementation of encoding starter dimensions and starter dimension values of a starter funnel.

FIG. 9 illustrates one implementation of encoding starter dimensions and starter dimension values of a starter funnel into a starter candidate individual/starter genome.

FIG. 10 depicts one implementation of initializing a first generation based on a starter funnel.

FIG. 11 shows a symbolic drawing of one implementation of procreating new candidate individuals using a crossover technique.

FIG. 12 is a symbolic drawing of one implementation of procreating candidate individuals using a mutation technique.

FIGS. 13 and 14 show one implementation of generating funnels of one or more web interfaces in dependence upon corresponding candidate individuals for frontend presentation to the end users.

FIG. 15 is one implementation of a machine learned user device (MLUD).

FIG. 16 illustrates one implementation of a machine learned content delivery network (MLCDN).

FIG. 17 graphically illustrates a control individual and a winning individual, comprising dimensions and dimension values.

FIG. 18 is a chart that graphically illustrates example results of one implementation of the disclosed evolutionary computations.

FIG. 19 shows one implementation of a risk tolerance module that implements risk tolerance for the disclosed evolutionary computations and an allocation module that segments the candidate population.

FIG. 20 illustrates using Bayesian posterior probability distributions of parent candidates A and B to determine a Bayesian prior probability distribution of a child candidate C.

FIG. 21A illustrates Bayesian posterior (Beta) probability distributions of a candidate and also illustrates a probability (of a particular candidate) to beat all of the other candidates.

FIG. 21B illustrates a function derived from a histogram of collected conversion age data that is used to account for delayed conversion rates when determining a Bayesian posterior conversion rate probability distribution of a candidate.

FIG. 22 is a simplified block diagram of a computer system that can be used to implement the technology disclosed.

DETAILED DESCRIPTION Introduction

The following discussion is presented to enable any person skilled in the art to make and use the technology disclosed, and is provided in the context of a particular application and its requirements. Various modifications to the disclosed implementations will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other implementations and applications without departing from the spirit and scope of the technology disclosed. Thus, the technology disclosed is not intended to be limited to the implementations shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

Examples of systems, apparatus, and methods according to the disclosed implementations are described in a conversion optimization context. In other instances, the technology disclosed can be applied to multivariate testing, AB testing, landing page optimization, conversion rate optimization, website testing, website optimization, search engine optimization, information technology, telecommunications systems, financial systems, security trading, banking, business intelligence, marketing, medical and health sciences, mining, energy, etc. Other services are possible, such that the following examples should not be taken as definitive or limiting either in scope, context, or setting.

The technology disclosed can be implemented in the context of any computer-implemented system including a database system, a multi-tenant environment, or a relational database implementation like an Oracle™ compatible database implementation, an IBM DB2 Enterprise Server™ compatible relational database implementation, a MySQL™ or PostgreSQL™ compatible relational database implementation or a Microsoft SQL Server™ compatible relational database implementation or a NoSQL™ non-relational database implementation such as a Vampire™ compatible non-relational database implementation, an Apache Cassandra™ compatible non-relational database implementation, a BigTable™ compatible non-relational database implementation or an HBase™ or DynamoDB™ compatible non-relational database implementation. In addition, the technology disclosed can be implemented using different programming models like MapReduce™, bulk synchronous programming, MPI primitives, etc. or different scalable batch and stream management systems like Amazon Web Services (AWS)™, including Amazon Elasticsearch Service™ and Amazon Kinesis™, Apache Storm™, Apache Spark™, Apache Kafka™, Apache Flink™, Truviso™, IBM Info-Sphere™, Borealis™ and Yahoo! S4™.

As used herein, the “identification” of an item of information does not necessarily require the direct specification of that item of information. Information can be “identified” in a field by simply referring to the actual information through one or more layers of indirection, or by identifying one or more items of different information which are together sufficient to determine the actual item of information. In addition, the term “specify” is used herein to mean the same as “identify”.

As used herein, a given signal, event or value is “in dependence upon” a predecessor signal, event or value of the predecessor signal, event or value influenced by the given signal, event or value. If there is an intervening processing element, step or time period, the given signal, event or value can still be “in dependence upon” the predecessor signal, event or value. If the intervening processing element or step combines more than one signal, event or value, the signal output of the processing element or step is considered “in dependence upon” each of the signal, event or value inputs. If the given signal, event or value is the same as the predecessor signal, event or value, this is merely a degenerate case in which the given signal, event or value is still considered to be “in dependence upon” or “dependent on” or “based on” the predecessor signal, event or value. “Responsiveness” of a given signal, event or value upon another signal, event or value is defined similarly.

As used herein, “concurrently” or “in parallel” does not require exact simultaneity. It is sufficient if the evaluation of one of the individuals begins before the evaluation of another of the individuals completes.

As used herein, the term “funnel” refers to a frontend of a candidate individual, which is perceivable, operable, and understandable by end users. In implementations, a funnel invites user interaction and responds to it. A funnel comprises one or more web interfaces. Some examples of web interfaces include, both control versions and variations of, webpages, websites, e-mails, mobile applications, desktop applications, digital advertisements, social media messages (e.g., Tweet™, Facebook post™), social media feed items, social media profiles, social media accounts, social media chat messages, generic chat messages, forms, auto-filled forms, and so on. In some implementations a funnel can include branching.

In implementations of the technology disclosed, a funnel is implemented in or across one or more Internet accessible data centers such as a website (a set of funnels), together with associated applications running behind the website. End users operate Internet-accessible client devices or clients (e.g., desktop computers, notebook computers, tablets, mobile devices, phones or other devices having rendering engines, or the like) that are capable of accessing and interacting with the funnel. In one implementation, the end users access the funnel by opening a web browser or a mobile application. In some implementations, the end users may authenticate to the funnel (or some portion thereof).

In implementations of the technology disclosed, a web interface is a structured document whose structure is dependent upon the underlying format. For example, in one implementation, a web interface has a web-supported format based on Hyper Text Markup Language (HTML), Extensible Markup Language (XML), or other web-supported structured document. The web interface may include one or more resources (e.g., a JavaScript resource, a Cascading Style Sheet (CSS) resource, an Asynchronous and JavaScript XML (AJAX) resource, an image resource, a video resource, etc.), or, more typically, references to such resources, embedded within the web interface. By way of example, a resource embedded in the web interface may generally be included or specified within a script element, style element, image element, or object element, among others, depending on the type of resource. Typically, a web browser or other client application executing at the client device of the end user constructs a document object model (DOM) representation of the received web interface.

In another implementation, the web interface has an e-mail format based on plain text, rich text, or HTML (with or without a style definition format such as CSS or scripting instructions in a format such as JavaScript, e.g., Microsoft Outlook™, Google Gmail™, Apple Mail™, iOS Mail™, Thunderbird™, AOL Mail™, Yahoo Mail™, Windows Live™). In yet other implementations, the web interface has a mobile application format based on HTML5, native formats (e.g., iOS™ or Android™), and hybrid formats.

Any other conventional or future-developed structured documents or formats thereof or used therein, are considered to be web interfaces. Such implementations will be readily apparent to those skilled in the art without departing from the spirit and scope of the technology disclosed (such as audio and haptic presentations of web interfaces).

The web interface (e.g., webpages, websites, e-mails, mobile applications, desktop applications, digital advertisements) comprises page elements. Some examples of page elements include images, videos, animations, headline, heading, calls-to-action, text copies, form length, and others. In one example, the calls-to-action define what happens when a user clicks or taps on any part of the web interface. The page elements of the web interface are arranged in a so-called web interface layout. A web interface layout defines the positioning of the page elements of the web interface relative to each other. For example, an image might be at the bottom right and a video in the center of the web interface layout. In the web interface, all the interchangeable page elements are described using the web interface layout, which describes how the page elements are supposed to appear relative to each other and how they are supposed to interact with each other. Each page element has several properties. For example, image page elements have properties related to type, placement, and size; calls-to-action have properties related to color, text, size, and placement; and text copies have properties related to content, length, and size.

As used herein, the terms “funnel(s)”, “candidate individual(s)”, “individual(s)”, “genome(s)”, and “chromosome(s)” are used interchangeably. Also, “individual(s)”, “genome(s)”, and “chromosome(s)” are composed of gene(s) and/or “gene value(s)”. As used herein, the term “gene(s)” and/or “gene value(s)” can refer to a different “entity” and/or different “entities” such as cluster(s) or page element(s) or element(s) or dimension(s) or starter dimension(s), page element value(s) or element value(s) or dimension value(s) or start dimension value(s) or value(s), sub-sequence(s), sub-element(s) or control sub-element(s) or test sub-element(s), sub-element value(s) or control sub-element value(s) or test sub-element values, or any combinations thereof. Accordingly, as used herein, “gene-by-gene” operations or operations conducted or carried out at the “gene-level”, such as gene-by-gene selection, gene-by-gene crossover, or gene-by-gene mutation can treat a single entity as a single unit to select, to crossover, and/or to mutate and/or or a combination of entities as a single unit to select, to crossover, and/or to mutate.

Machine Learned Conversion Optimization

The technology disclosed provides a so-called machine learned conversion optimization (MLCO) system that uses evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Website funnels with a single webpage or multiple webpages are represented as genomes. Genomes identify different dimensions and dimension values of the funnels. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes (genetic material) that are likely to perform well. Each webpage (e.g., genetic material) is tested only to the extent that it is possible to decide whether it is promising, i.e., whether it should serve as a parent for the next generation of genetic materials, or should be discarded.

In addition to webpages, genetic algorithms can be applied to many other types of environments. For example, genetic algorithms can be used in any environment in which a large amount of data can be or has been collected which records experience over time within the environment. Specifically, for example, a healthcare environment may record clinical data, diagnoses and treatment regimens for a large number of patients, as well as outcomes. A business environment may record customer information such as who they are and what they do, and their browsing and purchasing histories. A computer security environment may record a large number of software code examples that have been found to be malicious. A financial asset trading environment may record historical price trends and related statistics about numerous financial assets (e.g., securities, indices, currencies) over a long period of time. Genetic algorithms have been applied to all of the above-mentioned environments. With respect to stock categorization, for example, according to one theory, at any given time, 5% of stocks follow a trend. Genetic algorithms are thus sometimes used, with some success, to categorize a stock as following or not following a trend.

In ecommerce, designing user experiences, i.e., webpages and interactions, which convert as many end users as possible from casual browsers to paying customers is an important goal. While there are some well-known design principles, including simplicity and consistency, there are also often unexpected interactions between elements of the page that determine how well it converts. The same element may work well in one context but not in others—it is often hard to predict the result, and even harder to decide how to improve a given page.

An entire industry has emerged to tackle these challenges; it is called conversion rate optimization, or conversion science. The standard method most practitioners use is AB testing, i.e., designing two different version of the same page, showing them to different users, and collecting statistics on how well they each convert.

This process allows incorporating human knowledge about the domain and conversion optimization into the design, and then testing their effect. After observing the results, new designs can be compared and gradually improved. The A/B testing process is difficult and time-consuming. Only a very small fraction of page designs can be tested in this way, and subtle interactions in the design may simply be missed completely.

The technology disclosed automatically generates webpage candidates to be tested from the variables and variable values marketers themselves create. The variables and variable values can be anything on a website, from small changes like button color and font weight to whole-scale messaging and design specifications. These variables can all be on a single page or across multiple pages in a funnel. The technology disclosed searches for the most successful variables and variable values in a vast space of possible combinations of the values. Each candidate individual from the candidate pool can be viewed as identifying a particular solution to a problem and identifying a value for each of a plurality of dimensions of the particular solution. Further, each candidate individual can be viewed as representing a webpage configuration and the identified values for the plurality of dimension can be viewed as representing different characteristics of the webpage configuration.

With the technology disclosed, thousands of page designs can be tested in a short time, which is impossible through A/B testing. On the other hand, through evolutionary search, the technology disclosed tests only a small subset (e.g., thousands) of page designs out of a much larger set of page designs (e.g., millions) to find the best ones. The technology disclosed learns over time which combinations of elements are effective, and gradually focuses the search around the most promising designs.

The technology disclosed is an automated system for conversion optimization, capable of testing vastly more ideas in shortened time frames. It finds the subtle combinations of variables that lead to conversion increases. The technology disclosed can discover designs that convert better than those designed by humans, often because it finds unexpected interactions between elements. For instance, the technology disclosed can find that the button needs to be green, but only when it is transparent and the header is in small font and the header text is aligned. Such interactions often do exist, and they can be very difficult to find. The technology disclosed makes this discovery process automatic, based on artificial intelligence, instead of extensive human effort. With the technology disclosed, it is thus possible to optimize conversions better and at a much larger scale than before—and keep optimizing them as the e-commerce conditions change.

Reliability of Fitness Estimates

Evolutionary optimization is a parallel search method that gradually improves the quality of a population of candidates. The quality of a candidate is measured by a fitness function. At the end of the evolution, the candidate with the highest fitness is selected as a winner. Thus, the final result of the evolutionary run is to identify the candidate that is supposed to be the best in the population of candidates. In practice in many domains, however, the fitness can only be measured approximately, by testing each candidate with a number of samples. For instance, with respect to web interface design, each candidate is a web interface design that is shown to a number of users (such as 1,000) to estimate how well the web interface design converts browsing customers to purchasing customers. Such sampling results in a multiple hypothesis problem. Because several thousands of candidates are tested in this way, some of them will have high estimated conversion rates simply because they were lucky, i.e. by chance happened to receive many users that converted. If such a lucky candidate is selected as a winner, it will disappoint in the future because true conversion rate (that will be observed in the long run in the future) will be much lower. In some embodiments, all of the fitness estimates will be uncertain, unreliable, and/or stochastic.

In evolutionary computation, a set of new samples, called a validation set, is given to the top candidates, and the candidate that performs best on the validation set is selected. It is not known how many samples are needed to do this reliably and the multiple hypothesis problem still exists. This approach also requires more samples than can be used during evolution, and they need to be available after evolution, which is not always the case (e.g. in web interface design, no more users are available after the experiment is terminated).

Machine Learned Conversion Optimization (MLCO) System

FIG. 1 depicts a conversion system 104 that implements evolutionary computations to identify high performing candidate individuals in a candidate search space. The conversion system 104 does so by perpetually collecting and developing performance measures 106 for candidate individuals in the candidate individual population 102. The conversion system 104 also uses a presentation generator to transform the candidate individuals into funnels of one or more web interfaces. The funnels are presented to end users during live online evaluation 108 so that the performance measures 106 can be developed for the corresponding candidate individuals based on evaluation data (e.g., user clicks) provided by the end users.

Each funnel has a mechanism by which achievement of a target user behavior can be detected. In one implementation, the target user behavior is conversion by end users. For each funnel, conversion can be detected based on a conversion goal defined by a designer, marketer, advertiser, or content creator. Examples of a conversion goal include a user reaching a specific web interface of a funnel (e.g., a thank you page), a user clicking a link or button or other user interface element on a web interface of a funnel, or any other custom event defined by the designer (e.g., using jQuery). Other examples of a conversion goal include a user clicking a “Buy Now” button on Amazon.com™, a user clicking a “Sign Up” button on an e-mail registration page, a user clicking a “Download Now” button on an application landing page, a user filling out a form to download a report, a user signing up for a webinar, a user completing a purchase process, a user adding an item to a shopping cart, a user initializing a shopping cart checkout, and a user making a pledge to read a book. The conversion goals can be different for different funnels and different testing environments.

The conversion system 104 operates according to the performance measures 106, which indicate to the conversion system 104 how to measure the fitness of a candidate individual. The conversion system 104 optimizes for candidate individuals that have the greatest fitness, however fitness is defined by the performance measures 106. The performance measures 106 are an indication of success of a candidate individual and corresponding funnel in achieving a target user behavior. The performance measures 106 are specific to the environment and goals of the particular application. In a webpage testing environment, for example, the performance measures 106 can be a function of a conversion goal defined for a funnel. In one implementation, the performance measures 106 are based on a “conversion rate”. Conversion rate is a ratio of end users who visit a funnel and complete the conversion goal defined for the funnel (i.e., what percentage of the visitors complete the conversion goal). For example, a website with 5,000 visitors and 50 conversions has a conversion rate of 1%. In another implementation, the performance measures 106 are based on a “revenue rate”. Revenue rate is a revenue amount (e.g., numerical dollar value) generated per end user as a result of the end users completing the conversion goal defined for the funnel.

Regarding the live online evaluation 108, “live online evaluation” means real time tests or trials where funnels constructed in dependence upon the candidate individuals are presented to real world end users for testing. During the live online evaluation 108, the performance measures 106 are developed for each of the candidate individuals based on the real time and real-world user interaction with the corresponding funnels. Typically, for each funnel, the performance measures 106 are determined over a sample of end users (e.g., 1000 end users or 2000 end users, and so on). Note that the performance measures 106 calculated based on the performance of the funnels during the live online evaluation 108 are associated with or linked to or assigned to the corresponding candidate individuals originally used to generate the funnels. After that, the performance measures 106 are used by the disclosed evolutionary computations to procreate high performing individuals.

The conversion system 104 interacts with a database containing the candidate individual population 102. As used herein, the term “database” does not necessarily imply any unity of structure. For example, two or more separate databases, when considered together, still constitute a “database” as that term is used herein. In some implementations, the database can store information from one or more tenants into tables of a common database image to form an on-demand database service (ODDS), which can be implemented in many ways, such as a multi-tenant database system (MTDS). A database image can include one or more database objects. In other implementations, the database can be a relational database management system (RDBMS), object-oriented database management system (OODBMS), distributed file system (DFS), no-schema database, or any other data storing system or computing device.

In one implementation, the candidate individuals in the candidate individual population 102 are stored and managed by conventional database management systems (DBMS), and are accessed using SQL statements. Thus, a conventional SQL query can be used to obtain, for example, the performance measures 106 of the candidate individuals. New candidate individuals can be inserted into the candidate individual population 102 using the SQL “insert” statement, and candidate individuals being discarded can be deleted using the SQL “delete” statement. In another implementation, the candidate individuals in the candidate individual population 102 are stored in a linked list. In such an implementation, insertion of a new candidate individual can be accomplished by writing its contents into an element in a free list, and then linking the element into the main linked list. Discarding of candidate individuals involves unlinking them from the main linked list and re-linking them into the free list.

In an implementation a winner selector 112 selects a winning candidate or multiple winning candidates from the candidate individual population 102 when evolution is complete. Evolution can be complete (i) after a set number of evolutions of the candidates have been performed, (ii) at a point when a predetermined conversion rate is reached, or (iii) at any other time that is convenient. When evolution of the candidates is complete, there should be multiple candidates in the candidate individual population 102. The winning candidate or candidates are selected by the winner selector 112 (from the candidate individual population 102) based on Bayesian calculations as described herein. The winning candidate or candidates can represent a particular solution to a problem, such as identifying a webpage structure or set of formats that would likely result in the highest conversion rate or revenue.

In some implementations, the MLCO system uses an optional fulfillment system 110. Fulfillment system 110 is a system that is controlled automatically by output/action signals from the conversion system 104. In a webpage testing environment, for example, the fulfillment system 110 is a product distribution e-warehouse (e.g., Amazon.com™) that receives the signals via a computer network (not shown) and takes appropriate transactional and delivery actions.

The discussion now turns to the evolutionary computations implemented by the MLCO system of FIG. 1.

Evolutionary Computations

FIG. 2 illustrates elements that can be used to implement the evolutionary computations of the conversion system 104 in FIG. 1. In FIG. 2, solid lines indicate data flow, broken lines indicate control flow, and dotted lines indicate optional modules. The elements in FIG. 2 can be implemented in hardware or software, and need not be divided up in precisely the same blocks as shown in FIG. 2. Some of the elements can also be implemented on different processors or computers, or spread among a number of different processors or computers. In addition, it will be appreciated that some of the elements can be combined, operated in parallel or in a different sequence than that shown in FIG. 2 without affecting the functions achieved. The blocks in FIG. 2, designated as elements, can also be thought of as flowchart steps in a method. An element also need not necessarily have all its code disposed contiguously in memory; some parts of the code can be separated from other parts of the code with code from other elements or other functions disposed in between.

Population Initialization

Referring to FIG. 2, the candidate individual population 102 is initialized by a population initializer 202, which writes a preliminary pool of candidate individuals in the candidate individual population 102. The preliminary pool can be created randomly, or in some implementations, a priori knowledge can be used to seed the first generation. In another implementation, candidate individuals from prior runs can be borrowed to seed a new run. At the start, all candidate individuals are initialized with performance measures 106 that are indicated as undefined.

The population initializer 202 also defines a candidate search space. As used herein, the term “candidate search space” refers to a space having M dimensions, where each dimension represents an axis along which different candidate individuals can have different values. The size of the space in each dimension is equal to the range of values available for that dimension.

The following discussion describes how a starter funnel is defined by a designer, marketer, advertiser, or content creator using a design editor. Once defined, the starter funnel is used by the population initializer 202 to define the candidate search space and seed the first generation.

Starter Funnel Definition

The candidate search space is defined by the population initializer 202 in dependence upon at least one starter funnel, and the starter dimensions and starter dimension values of its starter web interfaces. In implementations, the starter funnel is defined using a design editor 302, shown in FIG. 3. Design editor 302 comprises various interface components as part of interface 304 that are used by a designer, marketer, advertiser, or content creator to implement a MLCO workflow. In particular, the design editor 302 is used by the designer to: specify one or more starter web interfaces of the starter funnel; set a conversion goal for the starter funnel; set a performance metric for the starter funnel; set starter dimensions and starter dimension values for each of the starter web interfaces of the starter funnel; preview the starter funnel; and deploy the test.

FIG. 4 shows one implementation of a starter funnel with three starter web interfaces (e.g., one landing page and two funnel pages). In one implementation, the starter web interfaces are identified in the starter funnel by their respective access paths (e.g., unified resource locators (URLs), and arranged in a topological sequence. Typically, the topological sequence determines a path an end user follows through a funnel to complete a conversion goal defined for the funnel. The topological sequence is set graphically by the designer across the interface 304 and stored logically in memory.

FIG. 4 also depicts a “define conversion goal” button that allows the designer to set a conversion goal for the starter funnel. Examples of a conversion goal also include “increase leads” or “increase revenue”. The designer also defines what type of events will be considered to indicate that a conversion has occurred, such as visiting a thank you page, clicking on a button or link, or a custom event. After setting the conversion goal, the designer specifies a performance metric for the starter funnel. The performance metric is used by the disclosed evolutionary computations to evaluate the performance of the candidate individuals during the test.

Then, a web interface layout is generated for the starter funnel. As used herein, a “web interface layout” is merely a template within which the alternative values for dimensions are inserted in order to define a particular web interface of a funnel. In one implementation, the web interface layout is displayed across a simulated device selected by the designer from among options of devices with varying screen canvases (e.g., smartphones, tablets, computers, wearable devices). In some implementations, the options of devices lists different device models like iPhone X™, Samsung Galaxy S8™ and others. FIG. 5 illustrates one implementation of a web interface layout and different dimensions and dimension values that can be applied to the web interface layout.

Then, the designer selects different page elements (referred to herein as “starter dimensions”) to be included in the web interface layout for each of the starter web interfaces of the starter funnel. In one implementation, the designer also specifies a type for a page element. For example, a name of the page element is “Clean, Awesome and Multi-Purpose” and the type of the page element is “Headline—Main”. Advancing further, the designer defines different sub-elements (also referred to herein as “starter dimensions”) for each of the starter web interfaces of the starter funnel. Examples of sub-elements include text, formatting/CSS (Cascading Style Sheets), page element concealment, page element removal, class, HTML, custom jQuery, and image. In some implementations, the designer can activate or deactivate some of the sub-elements (e.g., using a dropdown). In one implementation, the inactive sub-elements are displayed across the interface 304 with strikethrough. In other implementations, the design editor 302 automatically makes some sub-elements active and others inactive.

Then, the designer sets sub-elements values (referred to herein as “starter dimension values”) for each of the defined sub-elements. In addition, the designer also sets, for each of the defined sub-elements, a control sub-element value and one or more test sub-element values. In other implementations, the design editor 302 automatically sets a default control value for the sub-elements. For example, for the page element “Headline—Main”, the control sub-element value is set to be “Clean, Awesome and Multi-Purpose” and test sub-element values are set to be “Slick, Awesome and Powerful”, “New, Fast, Better”, “Shiny, Better than the Competition”, and “Best you can Buy”. Similarly, in other implementations, different page elements and corresponding sub-elements and sub-element values can be defined as a set of starter dimensions and starter dimension values of each of the starter web interfaces of the starter funnel.

Each web interface of a funnel can be thought of as a point in a vector-based search space. Each dimension axis in the vector space corresponds to one of the page elements, and each different coordinate position along a given axis indicates one of the designer-specified alternatives for that page element. For example, in FIG. 5, the call-to-action dimension has four coordinate positions (values or alternatives), namely “Go!”, “Go to the Auction!”, “Win the Auction!”, and “Win it NOW!”. A particular web interface of a funnel specifies a value for each of the page elements/dimensions.

FIG. 6 shows one implementation of a web interface of a funnel. In FIG. 6, the web interface is defined by the arrangement or position of different page elements/dimensions in the web interface layout, and corresponding properties or values of the page elements /dimensions, including, choice of picture (page element/dimension), background image (page element/dimension), background color (page element property/dimension value), headline copy (page element/dimension), typefaces and colors of the headline copy (page element property/dimension value), appearance or concealment of logo (page element/dimension), subhead copy (page element/dimension), typefaces and colors of the subhead copy (page element property/dimension value), description copy (page element/dimension), typefaces and colors of the description copy (page element property/dimension value), phone call-to-action (page element/dimension), type, format, and color of the phone call-to-action (page element property/dimension value), form design (page element/dimension), button (page element/dimension), and shape, color, and text of the button (page element property/dimension value).

Once created, a preview of the starter funnel, and its comprising starter web interfaces, is presented to the designer across the interface 304.

With the definition of the starter funnel understood, the discussion now turns to how a corresponding starter candidate individual is initialized by encoding the starter funnel at runtime.

Starter Individual Initialization

FIG. 7 is a symbolic drawing of one implementation of transforming an editor design representation 702 of a starter funnel into a runtime design representation 712. At runtime 322, the editor design representation 702 of the starter funnel defined by the designer is transformed into the runtime design representation 712. In the example used in FIG. 7, the editor design representation 702 includes a tree. The root of the tree is the highest level of granularity for which a test is performed. For a website or mobile webpage, the root is the domain. For mobile applications, the root is the application. For e-mails, the root is the e-mail; the subject and pages are next level of the tree. For other applications (such as advertising), different mappings can be used. In the domain of webpages and mobile applications, a website has one to n funnels; each funnel has a goal, and one to n pages. Each page has one to n clusters of elements. Each cluster has one to n elements. Each element has a set of sub-elements that change a property of the element. As used herein, sub-elements are elements too. Each sub-element has one to n values. In other implementations, the structure might be different. For example, elements might not be clustered, or the representation might not be hierarchical at all.

Also, at the runtime 322, the starter funnel is represented by a starter genome that encodes it as a binary formulation. In one implementation, the binary encoding is a concatenation of the leaves of the tree, which in the case of the starter funnel includes concatenating the starter dimensions and starter dimension values of each of the starter web interfaces of the starter funnel. In some implementations, prior to the genome encoding, a schematic representation of the editor designer representation 702 is generated in the form of runtime design representation 712. In other implementations, the genome encoding can be based on other schemes relying on data types other than binary data type (0 or 1), such as quantitative or numerical data type, qualitative data type, discreet data type, continuous data type (with lower and upper bounds), integers data type (with lower and upper bounds), nominal data type, ordinal or ranked data type, categorical data type, interval data type, and/or ratio data type. For example, the genome encoding can be based on, or any combination thereof, real values between 0 and 1, continuous values such as Red, Green, Blue (RGB) values between 0 and 256, hexadecimal values of CSS colors (e.g., #F0F8FF), categorical color values of CSS colors (e.g., AliceBlue), respective values of other CSS property groups and properties (such as the ones listed on the web at <http://www.w3schools.com/cssref/#animation>), size of a particular dimension (e.g., height and width), a set of different values and data types (e.g., different numeric dollar price values or a combination of different numeric dollar price values and heights and widths), and others.

FIG. 8 depicts a symbolic drawing of one implementation of encoding starter dimensions and starter dimension values of a starter funnel. In FIG. 8, clusters 802, elements 804, sub-elements 806, and sub-element values 808 corresponding to the sub-elements 806 of the starter funnel are encoded into a binary string encoding 810. In the binary string encoding 810, the left most bit for each sub-element value is the control value. In other implementations, the binary string encoding 810 can be based on other schemes relying on data types other than binary data type (0 or 1), such as quantitative or numerical data type, qualitative data type, discreet data type, continuous data type (with lower and upper bounds), integers data type (with lower and upper bounds), nominal data type, ordinal or ranked data type, categorical data type, interval data type, and/or ratio data type. For example, the binary string encoding 810 can be based on, or any combination thereof, real values between 0 and 1, continuous values such as Red, Green, Blue (RGB) values between 0 and 256, hexadecimal values of CSS colors (e.g., #F0F8FF), categorical color values of CSS colors (e.g., AliceBlue), respective values of other CSS property groups and properties (such as the ones listed on the web at <http://www.w3schools.com/cssref/#animation>), size of a particular dimension (e.g., height and width), a set of different values and data types (e.g., different numeric dollar price values or a combination of different numeric dollar price values and heights and widths), and others.

FIG. 9 illustrates the population initializer 202 encoding starter dimensions 902 and starter dimension values 912 of a starter funnel into a starter candidate individual/starter genome. In particular, the encoding produces a binary sequence representing a starter candidate individual 922. The encoding of the starter candidate individual 922 is shown below:

[  [ [0, 1], [0, 0, 1, 0] ], Logo [ 0, 0, 0, 1, 0 ], Main Headline [ Cluster  [ [1, 0], [0, 1] ], Sub Headline  [ [1, 0], [0, 1] ], Call-to-Action ] ]

Starter candidate individual 922 includes seven genes corresponding to each of the seven sub-elements 806. The left most bit of each gene is the control value. In other implementations, the encoding of the starter candidate individual 922 can be based on other schemes relying on data types other than binary data type (0 or 1), such as quantitative or numerical data type, qualitative data type, discreet data type, continuous data type (with lower and upper bounds), integers data type (with lower and upper bounds), nominal data type, ordinal or ranked data type, categorical data type, interval data type, and/or ratio data type. For example, the encoding of the starter candidate individual 922 can be based on, or any combination thereof, real values between 0 and 1, continuous values such as Red, Green, Blue (RGB) values between 0 and 256, hexadecimal values of CSS colors (e.g., #F0F8FF), categorical color values of CSS colors (e.g., AliceBlue), respective values of other CSS property groups and properties (such as the ones listed on the web at <http://www.w3schools.com/cssref/#animation>), size of a particular dimension (e.g., height and width), a set of different values and data types (e.g., different numeric dollar price values or a combination of different numeric dollar price values and heights and widths), and others.

With the initialization and encoding of the starter candidate individual understood, the discussion now turns to how the candidate search space is defined.

Candidate Search Space Definition

As discussed above, the candidate search space is defined based on the starter funnel. In particular, the population initializer 202 defines the candidate search space by subjecting the starter dimensions and starter dimension values of the starter funnel to combinatorial operations. In one example, if the designer specifies 4 starter page elements (e.g., logo, main headline, sub headline, call-to-action), 7 possible starter sub-elements, and 19 possible starter sub-element values, then a candidate search space with 640 possible combinations of dimensions and dimension values is defined by subjecting 8 4ifferent variations of the logo, 5 ferent variations of the main headline, 4 different variations of the sub headline, and 4 different variations of the call-to-action to combinatorial operations (i.e., 8×5×4×4=640).

With the definition of the candidate search space understood, we now turn to how various generations of the evolutionary computations disclosed herein are optimized.

Generation Optimization

Starter Funnel Based Optimization

In implementations, the population initializer 202 creates the preliminary pool of candidate individuals (e.g., a first generation) in dependence upon at least one starter funnel and the starter dimensions and starter dimension values of its starter web interfaces (e.g., starter candidate individual 922). In one implementation, candidate individuals in the first generation are initialized in a manner that ensures that each of the starter dimension values occurs in only one of the initialized individuals of the first generation. FIG. 10 depicts such an implementation. In FIG. 10, the starter candidate individual 922 has 7 genes and 19 possible gene values. As a result, in just the starter candidate individual 922, 7 of the 19 gene values are initialized. Accordingly, the population initializer 202 creates 12 additional genomes such that the remaining 12 possible gene values are initialized in the respective 12 additional genomes. In FIG. 10, the 12 possible gene values are depicted with a grey background. Thus, in the example where the candidate search space comprised 640 possible combinations of dimensions and dimension values, only a subset of the 640 possible combinations (e.g., 13 combinations in FIG. 10) is initialized in the first generation.

In another implementation, candidate individuals in the first generation are initialized in a manner that ensures that each of the starter dimension value occurs in at least one of the initialized individuals of the first generation.

In another example, assume that a starter genome is specified to include 28 starter dimension values for a set of starter dimensions. Note that some or all of the 28 starter dimension values can apply to the same starter dimension or to a set of different starter dimensions. Thus, in some instances, multiple starter dimension values from the 28 starter dimension values apply to a single starter dimension, which is a member of a group consisting of a plurality of starter dimensions. In the example shown in FIG. 5, the “headline font” starter dimension can have five different starter dimension values, namely “Audiowide”, “Impact”, “Cambria 20px”, “American Typewriter”, and “Copperplate”. Similarly, the “headline text” starter dimension can have five different starter dimension values as well, namely “Don't miss the live auction”, “Only a few minutes left! Live Auction”, “You can Win Your Car!”, “When it's gone, it's gone forever”, and “Win Your Dream Car!”. In the same vein, the “sub-headline background color” starter dimension can have four different starter dimension values, namely blue, cyan, orange, and red. Also, the “sub-headline text” starter dimension can have four different starter dimension values, namely “Go!”, “Go to the Auction!”, “Win the Auction!”, and “Win it NOW!”.

Accordingly, in one implementation, the first generation is initialized in a manner that ensures that each of the 28 starter dimension values is included in only one of the candidate individuals of the first generation. In another implementation, the first generation is initialized in a manner that ensures that each of the 28 starter dimension values is included in at least one of the candidate individuals of the first generation. In such implementations, multiple starter dimension values from the 28 starter dimension values are included in a single candidate individual of the first generation. This ensures that each of the starter dimensions and dimension values identified in one or more starter dimensions are provided as input to the disclosed evolutionary computations so that the performance measures 106 can be developed for them during the live online evaluation 108.

In other implementations, the candidate individuals are initialized by randomly selecting from the candidate search space an available value for a set of dimensions. In yet other implementations, the candidate individuals are initialized in a maximally uniform manner across the distribution of dimension values in the candidate search space in dependence upon a predetermined number of candidate individuals to be created. In one implementation, once created, a candidate individual is stored in the candidate individual population 102 as a vector having a particular value for each dimension.

Test Parameters Based Optimization

The evolutionary computations disclosed herein customize the MLCO in dependence upon one or more test parameters specific to a particular organization seeking promotion of the target user behavior. Examples of such test parameters include live user traffic registered or received by the organization (e.g., on a weekly, monthly, quarterly, or other calendar basis) and the performance measures 106 (e.g., conversion rate, revenue rate (determined over a sample of users)) registered by the organization. In implementations, values for such test parameters are provided by the organization or determined over an initial testing period.

Additionally, since live user traffic is a valuable commodity in a webpage testing environment, the evolutionary computations disclosed herein generate and process the various generations in an efficient manner. In one implementation, the evolutionary computations disclosed herein achieve such efficiency by ensuring that, given an amount of live user traffic received by an organization during a time period (e.g., in a week or month), a minimum number of evolutionary computation loops are completed within the time period to yield evolved candidate individuals that have been determined by the live online evaluation 108 to have superior values for the performance measures 106. So, in one example, if the amount of live user traffic received by an organization during a month is hundred thousand users (i.e., 100,000 users/month), then the evolutionary computations disclosed herein can be configured to generate and process at least three generations within the month.

In another implementation, such efficiency is achieved by ensuring that, given an amount of live user traffic received by an organization during a time period (e.g., in a week or month), a minimum number of candidate individuals are evolved (or subjected to the live online evaluation 108 over a sample of users) within the time period and/or only a limited number of candidate individuals are processed at each generation so that multiple generations are evolved within the time period. So, in one example, if the amount of live user traffic received by an organization during a month is hundred thousand users (i.e., 100,000 users/month), then the evolutionary computations disclosed herein can be configured to generate and process at least fifty candidate individuals across three generations within the month such that each candidate individual is live evaluated by at least two thousand users. In another example, the evolutionary computations disclosed herein can be configured to generate and process no more than seventeen candidate individuals at each of the three generations.

In yet another implementation, a specialized scheme for initializing the first generation includes making a maximum population size of the candidate individual population a function of the live user traffic and the performance measures 106 (e.g., conversion rate, revenue rate (determined over a sample of users). Furthermore, when a maximum population size calculated for an organization based on its live user traffic and performance measures 106 is not consistent with the number of starter dimensions and/or the number of starter dimension values specified by the organization's designer, the evolutionary computations disclosed herein use specialized selection schemes to select starter dimensions and starter dimension values for candidate individual initialization in a manner that ensures a population size of the candidate population at some (e.g., just the first generation) or at all generations is constrained by the maximum population size calculated for the organization. In one implementation, this is achieved by traversing the starter dimensions and/or the starter dimension values on a gene-by-gene basis and only selecting a subset of values in each gene and/or only a subset of genes for candidate individual initialization. In such an implementation, the selection of the subset of values in each gene and/or the subset of genes can be biased for earlier values and/or earlier genes or later values and/or later genes, can be uniform selection of genes and/or values at the gene-level, can be one-point selection of genes and/or values at the gene-level, can be two-point selection of genes and/or values at the gene-level, or can be random selection of genes and/or values at the gene-level. In other implementations, the selection of genes and/or values at the gene-level can be based on any other conventional or future-developed selection technique.

In another implementation, the maximum population size-based constraint is achieved by distributing selection of the starter dimensions and/or the starter dimension values across different generations such that some subsets of the starter dimensions and/or the starter dimension values specified by the organization's designer are selected in earlier generations and other subsets are selected at later generations. The selection of subsets can be uniform, one-point, two-point, random, or can be based on any other conventional or future-developed selection technique.

Assume, in one example, that given the amount of live user traffic received by an organization during a month is fifty thousand users (i.e., 50,000 users/month) and that each candidate individual is supposed to be live evaluated by at least two thousand users, the maximum population size determined for the organization is twenty-five candidate individuals across three generations. Also assume that the organization's designer has selected thirty starter dimensions, each with four starter dimension values. If the standard initialization scheme is implemented, which requires each starter dimension value to occur in only one candidate individual, a hundred and twenty starter dimension values would require at least ninety one (91) candidate individuals to be created in just the first generation. However, this would in conflict with the maximum population size of twenty-five calculated for the organization, which limits candidate individual count in a given generation to either eight or nine.

To resolve this conflict, the specialized initialization and selection schemes of the evolutionary computations disclosed herein can select a subset of the hundred and twenty starter dimension values and/or a subset of the thirty starter dimensions to initialize only eight candidate individuals in each of the first and second generations and only nine candidate individuals in the third generation, according to one implementation. In implementations, the selection can be random or biased towards earlier or later starter dimension values and/or starter dimensions. In other implementations, the starter dimension values and/or the starter dimensions that are not selected during the first generation can be included in the candidate population at the end of the second generation, or at any other subsequent generation.

In implementations, any stage and/or generation (e.g., first, second, third, fourth, tenth, or twentieth generation, and so on) of the evolutionary computations disclosed herein can utilize the specialized and/or the standard initialization schemes. As used herein, the term “initialization” or “initialize” refers to an evolutionary operation that can be applied at any generation of the evolutionary computations to create and/or procreate new candidate individuals.

With the population initialization understood, we turn to how the individuals are tested.

Testing Individuals

Candidate processing module 220 includes a candidate tester 212, a competition updater 222 and a procreator 228. The candidate tester 212 tests the candidate individuals in the candidate individual population 102. Each candidate individual undergoes a battery of tests or trials, each trial testing the candidate individuals on one or multiple samples of users with sample sizes ranging from hundreds, thousands, and millions of users. In another implementation, the number of tests or sample size is determined by parameters associated with the test. Examples of such test parameters include number of visitors per unit time, existing conversion rate, size of the candidate search space, preferred risk tolerance, and the type of performance measure. The tests or trials are implemented as the live online evaluation 108 where funnels generated in dependence upon the candidate individuals are presented to real world users for testing. Then, the performance measures 106 are collected based on the live online evaluation 108 during which the real-world users interact with the funnels. The candidate tester 212 updates the performance measures 106 associated with each of the tested candidate individuals on a real time basis. The frequency of the updates is also determined by the test parameters.

Fitness-Proportionate Procreation

To consume the live user traffic efficiently, the evolutionary computations disclosed herein, in some implementations, use specialized procreation schemes to create a second generation of individuals. In one implementation, a specialized scheme for procreating the second generation includes the procreator 228 subjecting genomes created during the first generation, i.e., parent genomes, to fitness-proportionate selection (e.g., roulette wheel selection, stochastic universal sampling). In such an implementation, every parent genome has a chance of being selected to breed, but fitter parent genomes are more likely to be chosen than weaker parent genomes. This is achieved by making a parent genome's selection probability a function of its fitness defined by the performance measures 106 (e.g., conversion rate, revenue rate (determined over a sample of users)). For example, the probability for selecting each candidate for procreation is proportionate to their fitness scores (i.e., candidate individual is fitness-proportionally selected from parent candidate individuals). For example, suppose there are 3 candidates with fitness scores of 2, 3 and 5, respectively. The probability for selecting them is then 20% (2/(2+3+5)), 30% ((3/(2+3+5))) and 50% ((5/(2+3+5))), respectively. In the case that 2 candidates out of these 3 candidates need to be selected, the fitness proportionate selection is performed once to select one candidate first, and then process is repeated with the first candidate eliminated. For example, the probability of selecting the other two candidates is 40% (2/(2+3)) and 60% (3/(2+3)), respectively.

In one implementation, the selection from a given parent genome is done on a gene-by-gene basis in dependence upon the selection probability of a particular gene. In some implementations, the particular gene's selection probability is a function of the performance measures 106 (e.g., conversion rate, revenue rate (determined over a sample of users)) of the genome that contains the particular gene. Accordingly, the resulting genomes in the second generation include genes selected from the parent genomes in dependence upon their respective selection probabilities.

In yet another implementation, the second generation is procreated by making random selections from the parent genomes on a gene-by-gene basis.

In implementations, any stage and/or generation (e.g., first, second, third, fourth, tenth, or twentieth generation, and so on) of the evolutionary computations disclosed herein can utilize the specialized procreation schemes.

After procreation, the candidate tester 212 operates again on the updated candidate individual population 102. The process continues repeatedly. In implementations, a controller 230 iterates the candidate tester 212 and the procreator 228.

Competition

In one implementation, the evolutionary computations utilize a competition updater 222, which is operationalized in certain generations. The competition updater 222 updates the candidate individual population 102 contents in dependence upon the updated performance measures 106. In some implementations, the competition updater 222 discards candidate individuals that do not meet a minimum baseline individual fitness (e.g., pre-set by an administrator or automatically set), or candidate individuals whose “individual fitness” relatively lags the “individual fitness” of similarly tested candidate individuals. The candidate individual population 102 is updated with the revised contents.

The competition updater 222 manages graduation of candidate individuals from one generation to the next. This process can be thought of as occurring one candidate individual at a time, as follows. First, a loop is begun through all candidate individuals for whom the performance measures 106 have been updated since the last time the competition updater 222 was executed. In one implementation, if the performance measures 106 for a current candidate individual are still below a baseline individual fitness (e.g., pre-set by an administrator or automatically set) or are sufficiently lagged relative to individual fitness of other candidate individuals, then the current candidate individual is discarded and the next one is considered. If the performance measures 106 for the current individual are above a baseline individual fitness (e.g., pre-set by an administrator or automatically set) or are relatively on par with individual fitness of other candidate individuals, then the current candidate individual is added to the next generation. The process then moves on to consider the next candidate individual in sequence. In an implementation, the competition updater 222 identifies the candidate individuals that are above or below baseline individual fitness using the average neighborhood fitness, as described in more detail below.

Relative Performance Measure

High performance measures of individuals with less experience could be due to luck rather than true fitness (e.g., due to unrealistic affinity of the users to an individual due to holiday season). That is, if compared to other individuals that have much more experience, younger, luckier individuals that have been determined to have high performance could still displace individuals whose fitness levels are lower but more realistic. Allowing such individuals to compete against each other solely on the basis of absolute fitness would optimize the evolutionary computations for individuals that are lucky.

A solution to this problem is that candidate individuals be compared based on their “relative performance measures”, instead of their absolute performance measures. A relative performance measure of a candidate individual is calculated based on the difference between an absolute performance measure of a control individual determined in a first time period and an absolute performance measure of the candidate individual also determined in the first time period. So, for example, if a first candidate individual created during a first generation has an absolute average conversion rate of 40% and a first control individual also initialized in the first generation has an absolute average conversion rate of 35%, then the relative performance measure of the first candidate individual is 5% (40%−35%=5%). Similarly, if a second candidate individual created during a second generation has an absolute average conversion rate of 45% and the first control individual's absolute average conversion rate in the second generation has increased to 42%, then the relative performance measure of the second candidate individual is 3% (45%−42%=3%). Accordingly, the first and second candidate individuals are compared based on their respective relative performance measures and the first candidate individual is found to be 2% better than the first candidate individual (5%−3%=2%), even though the absolute performance measure of the second candidate individual is 5% (45%−40%=5%) higher than that of the first candidate individual. Each comparison made by the competition updater 222 between the relative performance measure of one individual and that of another is sometimes referred to herein as a comparison “instance”.

In one implementation, the competition updater 222 determines whether the relative performance measure of the current candidate individual exceeds that of the least fit candidate individual in the candidate individual population 102. If so, then the least fit candidate individual is discarded, and the current candidate individual is moved to the next generation. If not, then the current candidate individual is discarded. The process then moves on to consider the next candidate individual in sequence.

Crossover and Mutation Based Procreation

Regarding procreation, any conventional or future-developed technique can be used for procreation. In an implementation, conditions, outputs, or rules from parent individuals are combined in various ways to form child individuals, and then, occasionally, they are mutated. The combination process for example may include crossover—i.e., exchanging conditions, outputs, or entire rules between parent individuals to form child individuals. New individuals created through procreation begin with performance measures that are indicated as undefined.

The procreator 228 adds to the candidate individual population 102 new individuals formed in dependence upon a respective set of one or more parent individuals from the candidate individual population 102 and not yet selected for discarding by the competition updater 222. In one implementation, a third generation of genomes is created by selecting pairs of parent genomes from the second generation and subjecting the parent genome pairs to a crossover scheme. In other implementations, procreation (e.g., crossover and/or mutation) can be initiated at any other generations, such as the first, second, fourth, or tenth generation, and so on.

Crossover

Regarding crossover, parent genome pairs are identified for crossover when a subsequently selected second parent genome differs from a first parent genome selected from the candidate population immediately before the second parent genome. In implementations, a parent genome can be selected to participate in multiple crossover operations during the same generation. In some implementations, within a given generation, a variety of crossover schemes are used to carry out crossover between parent genome pairs until a count of offspring genomes reaches the maximum population size determined for the given generation. In one implementation, the crossover occurs on a gene-by-gene basis. So, in one example, parent genomes can crossover based on sub-elements 806 and/or sub-element values 808, shown in FIG. 8. In other implementations, the crossover schemes applied at the gene-level to evolve a particular generation can be based on a variety of crossover schemes such as uniform crossover (e.g., selecting gene values alternatively from the parent genome pairs), one-point crossover, two-point crossover, and/or random crossover. In yet other implementations, the crossover schemes applied at the gene-level to evolve a particular can be based on any other conventional or future-developed crossover technique.

FIG. 11 shows a symbolic drawing of one implementation of procreating new candidate individuals using a crossover technique. In FIG. 11, a binary sequence of parent candidate individual A represents logo A with first text type (e.g., Times Roman), main headline A, sub headline A, and action button A. Also, in FIG. 11, a binary sequence of parent candidate individual B represents logo B with second text type (e.g., Arial), main headline B, sub headline B, and action button B. In one exemplary implementation, the procreator 228 creates a crossover child individual AB1 that includes logo A and sub headline A from parent candidate individual A, and includes main headline B and action button B from parent candidate individual B. In another exemplary implementation, the procreator 228 creates a crossover child candidate individual AB2 that includes logo A (from parent candidate individual A) with second text type (e.g., Arial) (from parent candidate individual B) and sub headline B from parent individual B, and also includes main headline A and action button A also from parent candidate individual A.

Mutation

FIG. 12 is a symbolic drawing of one implementation of procreating candidate individuals using a mutation technique. In FIG. 12, a first gene of a parent genome is mutated such that text type of logo A is mutated from a first text (e.g., Times Roman) to a second text (e.g., Arial).

Preferably, procreation involves crossover to create a small predetermined number of new individuals followed by mutation of only a small subset of the new individuals. Mutation does not increase the number of candidate individuals in this implementation; it merely modifies individuals that were created by crossover.

The evolutionary computations disclosed herein use mutation to avoid or overcome local maxima. Accordingly, in some implementations, the evolutionary computations disclosed herein are configured to carry out mutation only after carrying out crossover in a certain number of generations and/or after carrying out crossover in a certain number of procreation operations within the same generation (e.g., mutation occurs only after the third generation). In other implementations, procreation (e.g., crossover and/or mutation) can be initiated at any other generations, such as the first, second, third, fourth, or tenth generation, and so on.

In implementations, each parent genome is assigned a genome mutation probability that determines the likelihood of a parent genome being subjected to mutation at all during a generation. The “genome mutation probability” can be based on any probability scheme such as random probability, uniform probability, weighted probability, and/or fitness-proportionate probability. When a parent genome is selected, the mutation occurs on a gene-by-gene basis, according to one implementation. So, in one example, parent genomes can be mutated based on sub-elements 806 and/or sub-element values 808, shown in FIG. 8.

Within a parent genome, the selection of genes and/or gene values for the gene-by-gene mutation can be based on a “gene mutation probability”. The gene mutation probability can be based on any probability scheme such as random probability, uniform probability, weighted probability, and/or fitness-proportionate probability. So, for example, a Boolean or binary or vector encoding of a genome (such as the ones shown in FIG. 12) can be mutated on a gene-by-gene basis based on any probability scheme. In other implementations, the mutation at the gene-level can be uniform mutation, non-uniform mutation, boundary mutation, and/or gradient mutation. In yet other implementations, the mutation at the gene-level can be based on any other conventional or future-developed mutation technique.

With the procreation of individuals understood, we now turn to how the evolutionary computations disclosed herein accelerate traversal of the candidate search space using pseudo-evolution.

Accelerated Pseudo-Evolution

Performance efficiency is paramount for effective implementation of MLCO. The evolutionary computations disclosed herein add performance efficiency to the evolutionary process by accelerating traversal of the candidate search space (e.g., small search spaces). As discussed above, the candidate search space identifies all possible combinations of dimensions and dimension values; however only a subset of the combinations is initialized in each generation. Following the example used above, if the candidate search space identifies 640 combinations of dimensions and dimension values, then only 13 combinations are initialized in the first generation. Furthermore, whenever a previously generated combination (i.e., candidate individual or genome) is re-generated during a generation or between generations, it is identified as a duplicate and rejected. An evolutionary event or operation that produces a duplicate is referred to herein as a “collision”. Collisions cause performance inefficiencies because the results of the collisions (i.e., duplicates) are rejected and do not advance the evolutionary process.

In one example, a spike in a collision count can occur when a majority of the all possible combinations of dimensions and dimension values have already been generated and the evolutionary operations frequently create duplicates. As a result, it could take substantial time to generate the remaining combinations of dimensions and dimension values. Following the example used above, if 620 of the 640 possible combinations are created over 50 generations, then routine evolution could take another 20-50 generations to create the remaining 20 combinations.

The evolutionary computations disclosed herein solve this technical problem by switching to pseudo-evolution when a collision count reaches a pre-set threshold. In one implementation, the evolutionary computations disclosed herein track which of the all possible combinations of dimensions and dimension values have already been generated (e.g., using an annotation and/or enumeration scheme), and when the collision count reaches the pre-set threshold, they randomly generate only those combinations (i.e., candidate individuals or genomes) that have not been yet generated. Accordingly, the evolutionary process is made to artificially focus only on those combinations that have not been selected or created during any of the preceding generations. This is referred to herein as “pseudo-evolution”. Pseudo-evolution accelerates the evolutionary process because it bypasses the routine evolution (e.g., terminates, parallelizes, overrides, or avoids initialization and/or procreation schemes) and creates the remaining combinations in much fewer generations. Following the example used above, if 620 of the 640 possible combinations are created over 50 generations and the collision count reaches a threshold of 10, then the pseudo-evolution can exclusively utilize the next 2 generations to only create the remaining 20 combinations.

Evolution Pseudo Code

The following pseudo code shows one implementation of the evolutionary computations:

set_evolution_parameters create_candidates_generation_1 repeat repeat test_candidates until candidate_spread_is_sufficient or remove_criteria_is_met remove_low_performing_candidates create_new_candidates

-   -   Until performance_is_sufficient or no_more_designs_are_possible

The following Extended Backus-Naur Form shows another implementation of the evolutionary computations:

genome ::= [ element_cluster+ ] element_cluster ::= [ element+ ] | element element ::= [ action+ ] action ::= [ control, action_value ] action_value ::= text | formatting | html | hid | remove | image | class | custom text, formatting, html, image, class, custom ::= bit_value hide, remove ::= bit_value bit_value ::= 0 | 1

The discussion now turns to how candidate individuals are used to generate funnels and comprising web interfaces for frontend presentation to the end users.

Frontend Presentation

FIGS. 13 and 14 show one implementation of generating funnels of one or more web interfaces in dependence upon corresponding candidate individuals for frontend presentation to the end users. FIG. 13 shows four candidate individuals (CI 1 to CI 4) based on which four web interfaces are generated. Individuals (CI 1 to CI 4) can be any candidate individuals in the candidate individual population 102, i.e., they can be the candidate individuals for whom the performance measures 106 are developed during the live online evaluation 108.

FIG. 13 also shows that each genome is represented by a binary sequence (string). Each binary sequence is further compartmentalized into sub-sequences (sub-strings) referred to herein as “genes”. Each gene represents a dimension and corresponding dimension value for a funnel (e.g., characteristics of a webpage or a funnel including webpages). Accordingly, each gene identifies a sub-element and corresponding sub-element value for the web interfaces that are to be generated in dependence upon the genomes (CI 1 to CI 4). In implementations where the web interfaces are frontend (e.g., graphical) objects comprising content, the genes identify frontend (e.g., graphical) elements/components and corresponding element/component values that are to be included in the web interfaces generated based on the genomes (CI 1 to CI 4).

Note that in the implementation of FIGS. 13 and 14, each element of a funnel is encoded as a fixed-length substring of bits grouped as genes. For example, the sub headline element has four bits grouped into two genes. For each gene, only one of the bits can be active or “hot” (e.g., represented by “1”) so as to identify a particular dimension value from a set of available dimension values. The leftmost bit represents the “control” value of a gene, which, in one example, for main headline might indicate “plain text”. The second, third, and forth bits might, for example, indicate “bold”, “italics”, and “bold underlined”, respectively. It will be appreciated that many other encodings are possible in different implementations. For example, a 2-bit numeric value might be used, with “0” indicating the control, “1” indicating “bold”, “2” indicating “italics”, and “3” indicating “bold underlined”.

In other implementations of FIGS. 13 and 14, a genome can be represented and comprising elements encoded using other schemes relying on data types other than binary data type (0 or 1), such as quantitative or numerical data type, qualitative data type, discreet data type, continuous data type (with lower and upper bounds), integers data type (with lower and upper bounds), nominal data type, ordinal or ranked data type, categorical data type, interval data type, and/or ratio data type. For example, the encoding of the genomes and the comprising elements in FIGS. 13 and 14 can be based on, or any combination thereof, real values between 0 and 1, continuous values such as Red, Green, Blue (RGB) values between 0 and 256, hexadecimal values of CSS colors (e.g., #F0F8FF), categorical color values of CSS colors (e.g., AliceBlue), respective values of other CSS property groups and properties (such as the ones listed on the web at <http://www.w3schools.com/cssref/#animation>), size of a particular dimension (e.g., height and width), a set of different values and data types (e.g., different numeric dollar price values or a combination of different numeric dollar price values and heights and widths), and others.

The genomes (CI 1 to CI 4) are generated and provided by the conversion system 104 using the disclosed evolutionary computations, as discussed above. The conversion system 104 then utilizes a presentation database 1320 and a presenter 1310 to transform the genomes (CI 1 to CI 4) into funnels of one or more web interfaces.

The presentation database 1320 serves as a rule repository that identifies a frontend element value for each of at least one available value of each of the dimensions of the funnel. The presenter 1310 applies a selected one of the candidate individuals to the presentation database 1320 to determine frontend element values corresponding to dimension values identified by the selected candidate individual. Accordingly, the presenter 1310 is able to evaluate a genome provided by the conversion system 104 against the presentation database 1320 and generate an appropriate variation of the funnel based on the genome.

Presentation database 1320 includes various element rules. In the presentation database 1320, element rules are provided for all possible dimensions in the candidate search space, i.e., all possible frontend (e.g., graphical) elements/components that can be included in a web interface. Also, each element rule includes pointers identifying all possible dimension values or page element/component values for a given dimension or page element/component. For example, a “sub headline” element rule includes pointers for all possible properties of the “sub headline” element, such as color, text, size, placement, and the like.

For a selected genome, the presenter 1310 accesses the presentation database 1320 and evaluates the applicable element rule for each gene in the selected genome. In FIG. 13's example, element 1 rule is identified for all the logo genes, element 2 rule is identified for all the main head line genes, element 3 rule is identified for all the sub headline genes, and element 4 rule is identified for all the action button genes. Further, each element rule identifies a pointer for the respective gene values and generates corresponding frontend (e.g., graphical) elements/components and element/component values.

In one implementation, when one of the available values for each of the dimensions is a default value, the resulting the funnel has a default frontend element value for the default dimension value of each of the dimensions. In such an implementation, the presenter 1310 applies the selected candidate individual to the presentation database 1320 to determine frontend element values corresponding to all dimension values identified by the selected candidate individual other than the default dimension value for each of the dimensions.

In other implementations, a funnel customization specification memory is utilized, which stores, for a particular variation of the web interface funnel, a value for each of the dimensions of the funnel. In such an implementation, the presenter 1310 presents toward a user a funnel having frontend element values indicated by the presentation database 1320 as corresponding to the dimension values stored in the funnel customization specification memory. The funnel customization specification memory is also updated with a new set of values for each of the dimensions of the funnel. Updated values for the funnel customization specification memory are retrieved in response to user behavior for entering the funnel (e.g., user providing a landing page URL via a browser). In addition, the presenter 1310 accesses the presentation database 1320 in response to user behavior (e.g., user providing a landing page URL via a browser).

In one implementation, when one of the available values for each of the dimensions is a default value, the funnel has a default frontend element value for the default dimension value of each of the dimensions. In such an implementation, the funnel presented toward the user by the presenter 1310 has frontend element values indicated by the presentation database 1320 as corresponding to all of the dimension values identified in the funnel customization specification memory other than the default dimension value for each of the dimensions.

In FIG. 14, four web interfaces (WI 1 to WI 4) are generated by the presenter 1310 in dependence upon each of the candidate individuals (CI 1 to CI 4). Each of the web interfaces include frontend (e.g., graphical) elements/components and element/component values identified by the rules in the presentation database 1320. Then, the web interfaces (WI 1 to WI 4) are presented to the users for the live online evaluation 108.

Machine Learned User Device (MLUD)

FIG. 15 illustrates one implementation of an improved user device, referred to herein as the “machine learned user device” (MLUD). In FIG. 15, an end user uses a browser running on a MLUD 1504 to access a vendor website. The vendor web server 1502 is configured to deploy a conversion snippet, as part of a webpage, to the MLUD 1504 in response to the end user accessing the vendor website. After the conversion snippet is sent to the MLUD 1504 and before the webpage is rendered to the end user, the conversion snippet invokes a network server infrastructure. The network server infrastructure includes the conversion system 104, the candidate individual population 102, and a content delivery network (CDN) 1508.

The conversion snippet, deployed at the MLUD 1504, retrieves a presentation file 1330 from the CDN 1508 and stores the presentation file 1330 at the MLUD 1504. The CDN 1508 is pre-loaded with the presentation file 1330. Examples of common CDN services today include Akamai™, CloudFlare™, CloudFront™, Fastly™, MaxCDN™ KeyCDN™, Incapsula™, and GlobalDots™. The presentation file 1330 includes the presentation database 1320 and the presenter 1310, discussed above.

Then, the conversion snippet, deployed at the MLUD 1504, requests a genome from the conversion system 104. In response, the conversion system 104 retrieves a genome from the candidate individual population 102 and delivers the selected genome to the MLUD 1504.

Then, the MLUD 1504, having received the selected genome from the conversion system 104 and the presentation file 1330 from the CDN 1508, executes the presenter 1310. The presenter 1310 evaluates the selected genome against the rules of the presentation database 1320 and generates a funnel of one or more web interfaces, as discussed above. Accordingly, a standard user device is improved to generate algorithmically evolved web interfaces.

Machine Learned Content Delivery Network (MLCDN)

FIG. 16 illustrates one implementation of an improved content delivery network (CDN), referred to herein as the “machine learned content delivery network” (MLCDN). In FIG. 16, end users use browsers running on user devices 1602 to access a vendor website. The vendor web server 1502 is configured to deploy a conversion snippet, as part of a webpage, to the user devices 1602 in response to the end users accessing the vendor website. After the conversion snippet is sent to the user devices 1602 and before the webpage is rendered to the end users, the conversion snippet invokes a network server infrastructure. The network server infrastructure includes the conversion system 104, the candidate individual population 102, and a MLCDN 1604.

The MLCDN 1604 is pre-loaded with the presentation file 1330. The presentation file 1330 includes the presentation database 1320 and the presenter 1310, discussed above.

Then, the MLCDN 1604 requests a genome from the conversion system 104. In response, the conversion system 104 retrieves a genome from the candidate individual population 102 and delivers the selected genome to the MLCDN 1604.

Then, the MLCDN 1604, having both the selected genome and the presentation file 1330, executes the presenter 1310. The presenter 1310 evaluates the selected genome against the rules of the presentation database 1320 and generates a funnel of one or more web interfaces, as discussed above. The MLCDN 1604 then delivers the web interfaces to the user devices 1602 for frontend presentation to the end users (e.g., for live use by end users). Accordingly, a standard CDN is improved to generate algorithmically evolved web interfaces.

Example Results

During the live online evaluation 108, the performance measures 106 are gathered for each of the funnels based on user interaction 324. In some implementations, the interaction metadata can be tracked for each user on a session-by-session basis. For example, for each of the interacting users like user_m, user_j, user_(j+1), and user_n, corresponding sessions interaction_im, interaction_ij, interaction_i(j+1), and interaction_in can be created during the live online evaluation 108. During these sessions, funnels corresponding to candidate individuals like individual_i, individual_i+1, and individual_n can be presented to the users and evaluated in parallel. Based on the user interaction 324, the performance measures 106 for each of the candidate individuals can be collected and developed.

FIG. 17 graphically illustrates a control individual and a winning individual, and comprising dimensions and dimension values. In the example shown in FIG. 17, the best performing winning headline page element is shown along with the control banner.

FIG. 18 is a chart that graphically illustrates example results of one implementation of the disclosed evolutionary computations carrying out the MLCO. In FIG. 18, the chart shows that the best performing winning headline page element of FIG. 17 can improve the conversion rate from nearly 0% to 4.44%.

Risk Tolerance & Population Segmentation

Existing conversion optimization solutions, which involve live testing on real users, sometimes are cancelled in the very early stages when they cause conversions to drop. The risk and returns of conversion optimization are “inversely related”. As a result, the MLCO disclosed herein provides systems and methods of increasing conversions within the scope of the desired risk tolerance. FIG. 19 shows one implementation of a risk tolerance module that implements risk tolerance for the disclosed evolutionary computations. In the example shown in FIG. 19, the risk tolerance module is depicted in the form of a knob 1904 that can serve as a configurable parameter accessible across an administrative interface.

In one implementation, a risk tolerance level is specified that determines what percentage or amount of the live user traffic (i.e., what number of users) is presented algorithmically evolved funnels or candidate individuals and what percentage or amount of the live user traffic (i.e., what number of users) is presented control funnels or candidate individuals. For example, at a conservative risk tolerance level, only 5% of the webpages served to the user are determined by the evolutionary computations, whereas 95% of the webpages are control versions. In other implementations, different risk tolerance levels can be specified such as moderately conservative (e.g., 15% evolved webpages, 85% control webpages), moderate (e.g., 20% evolved webpages, 80% control webpages), moderately aggressive (e.g., 60% evolved webpages, 40% control webpages), and aggressive (e.g., 70% evolved webpages, 30% control webpages). For each of these risk tolerance levels, appropriate division of user exposure to algorithmically evolved candidate individuals and control candidate individuals can be set.

In one implementation, candidate individuals in the candidate individual population 102 can be segmented by an allocation module 1902 into multiple groups or pools in dependence upon a plurality of heuristics. Examples of such heuristics include how good a candidate individual is based on the performance measures (e.g., conversion rate or revenue rate), how experienced a candidate individual is based on the degree of live user evaluation or how recently a candidate individual was created or how many generations a candidate individual has survived or the number of times a candidate individual has been preserved due to being selected into the elitist pool, or whether the candidate individual is a control individual based on designer specification.

In one implementation, a first population group within the candidate population comprises only of control individuals, a second population group with the candidate population comprises only of elite individuals, a third population within the candidate population comprises only of non-elite pre-existing individuals (i.e., formed in a previous generation), and a fourth population within the candidate population comprises only of newly formed individuals (i.e., formed in a current generation).

Depending on the configuration of the knob 1904, frequency of alternative deployment of candidate individuals from an elitist pool 1912 and a control pool 1922 can be set. For example, if the knob 1904 is set at 20%, then the conversion system 104 accesses and deploys candidate individuals from the control pool 1922 80% of the time. In other implementations, different access frequency rules can be set that will be readily apparent to those skilled in the art.

Additionally, the live user traffic can be allocated to various groups or pools of the candidate population by the candidate tester 212 working in conjunction with the knob 1904. In one example, 50% of the live user traffic can be assigned to control individuals in a control pool, 30% of the liver user traffic can be assigned to elite individuals in an elite pool (e.g., the fittest members of the previous generation and preserved into the next generation), and 20% of the live user traffic can be assigned to non-elite individuals (e.g., recently created offspring genomes) in a non-elite pool. Furthermore, varied distribution of the live user traffic across different segments of the candidate population can be used by organizations to implement risk tolerance schemes. For instance, since elite individuals are superior to control individuals, an aggressive risk tolerance scheme can include routing a majority of the live user traffic to elite individuals in the elite pool and routing only minority of the live user traffic to the control pool and the non-elite pool. For example, 60% of the live user traffic can be assigned to the elite pool, 20% of the liver user traffic can be assigned to the control pool, and 20% of the live user traffic can be assigned to the non-elite pool. With such a scheme, because more users will be exposed to elite web interfaces, the organizational performance measures (e.g., organizational conversion rate or organizational revenue rate) improve faster. Similarly, in other implementations, conservative or semi-aggressive or semi-conservative schemes can be selected and/or implemented by the organizations.

In yet other implementations, different heuristics can be used to segment the candidate population (e.g., weighted probability, region, domain name, department within an organization, etc.), such that the live user traffic can be variedly distributed to the different segments, groups, pools, or clusters of the candidate population defined by the heuristics.

Selecting a Winner

Given that many candidates are produced and saved in the candidate individual population 102 during an evolutionary run, eventually a winning candidate or candidates should be chosen for long term implementation (i.e., implementation as the winning candidate and/or implementation for future procreation).

Turning back to FIG. 2, the winner selector 112, as controlled by the controller 230, selects a winner or multiple winners for implementation (i) after the evolutionary algorithm is complete (i.e., after the genes are finished evolving) or (ii) for future procreation during the evolutionary process. The winner selector 112 will perform “winner selection” to select one or more candidates from the candidate individual population 102. Note that throughout this document, when referring the “winner selection,” the selected winner (candidate) or winners (candidates) can be used for implementation after the evolutionary algorithm is complete, can be used for further developing the evolutionary algorithms by procreation, and can be used for any other purpose where a selected winner or winners would be more beneficial than a random selection of a candidate.

One problem of winner selection is that performance of the candidates has been estimated through sampling, up to this point. This creates a multiple hypothesis problem, meaning that a candidate that seems to perform the best may have simply gotten lucky during the sampling (i.e., the creating of the candidate individual population 102 and/or the elitist pool 1912). As a result, a candidate that seems to be the best solution might perform poorly in the future in long term implementation.

A solution to this problem includes solving three sub-problems: (1) deciding which candidate or candidates are the best candidate or candidates, (2) estimating performance of the best candidate or candidates and (3) confirming that the performance of the best candidate or candidates is statistically significantly better that that of other candidates, including control candidates.

There are potentially several ways to solve these problems, including age-layering (see Shahrzad et al., Estimating the Advantage of Age-Layering in Evolutionary Algorithms, 2016; incorporated herein by reference), performing successive A/B tests on the best candidates (see Kohavi et al., Online Controlled Experiments and A/B Tests, 2016; incorporated herein by reference), nonparametric functional estimation (see Shahrzad et al., Nonparametric Functional Estimation, 2014; incorporated herein by reference) and statistical analysis (e.g., Bayesian statistics and calculations). The technology disclosed implements Bayesian techniques to determine probabilities, as opposed to, for example Frequentist techniques which often times provide false positives, require multiple comparisons and multiple hypotheses.

Bayesian techniques (i.e., Bayesian calculations, Bayes theory, Bayes rule or Bayes law) can be used to determine an estimated probability distribution of an event, based on prior knowledge of conditions that might be related to that event (for example see: “Bayes Theorem,” https://en.wikipedia.org/wikiBayes%27_theorem; and “Bayesian Inference” https://en.wikipedia.org/wikiBayesian_inference, accessed Jul. 26, 2019 and incorporated herein by reference). Essentially, Bayes theory is probability theory that uses “Prior” probabilities (currently known or estimated probabilities) and current observations to calculate “Posterior” probabilities that estimate the probability of a particular event.

The technology disclosed implements Bayesian techniques to determine the probability of success of being a good solution to a problem or the probability of a webpage being successful for a particular candidate (genome). For example, a webpage may be considered successful when it “coverts” a shopper that is browsing to a buyer, or to putting an item into a shopping cart. Specifically, the technology disclosed calculates a Bayesian posterior conversion rate probability distribution for a particular candidate using a Bayesian prior conversion rate probability distribution calculated for the particular candidate and a Bayesian likelihood conversion rate probability distribution calculated for the particular candidate. These Bayesian posterior, prior and likelihood distributions need not be tied to “conversion rate” probabilities. They can also be tied to “solution rate” probabilities, which is the rate at which the particular candidate would be estimated to solve a particular problem. These techniques can be implemented in a particular manner to select a winner or winners from a candidate pool, such as the candidate individual population 102 or the elitist pool 1912.

According to Bayesian techniques, the probability of something happening (e.g., conversion from a shopper to a buyer; or providing a solution to a particular problem) is

${P\left( {H\text{|}E} \right)} = {\frac{{P\left( {E\text{|}H} \right)}{P(H)}}{P(E)}.}$

Generally, P (H|E) is the Bayesian “posterior probability distribution” of H (a hypothesis) occurring given event (or data) E after event E is observed. In terms of the technology disclosed the posterior probability distribution describes the probability distribution of a fixed but unknown solution (e.g., conversion) rate. As mentioned above, the technology disclosed is applicable to other problems and solutions aside from converting a shopper to a buyer. However, for the sake of simplicity, hereinafter the examples given are with regards to converting the shopper to the buyer, but conversion and conversion rate can be used interchangeably with solution and solution rate.

With respect to the technology disclosed P (H|E) is the posterior probability distribution of the true conversion rate (also referred to as a posterior conversion rate probability distribution) for a particular candidate, given data E. H is the true conversion rate of the candidate individual that we are trying to determine and data E is data that has been collected, such as number of previous conversions and visitor data, such as number of visitors. In other words, P (H|E) is the posterior probability distribution of what true conversion rate H might be after seeing data E. Put another way, the posterior probability distribution of a particular candidate individual predicts a future performance of the particular candidate individual. This posterior probability distribution is calculated in dependence upon a Bayesian “prior probability distribution” (discussed below in more detail) calculated for the particular candidate individual and a Bayesian “likelihood probability distribution” (discussed below in more detail) calculated for the particular candidate individual.

Generally, P (H) is the Bayesian “prior probability distribution” or the estimate of the probability of H (the hypothesis) before the event E has occurred. The concept of a prior probability distribution makes Bayesian techniques different than Frequentist techniques. According to Bayesian techniques, the prior probability distribution is given by a Beta distribution for convenience so that the posterior distribution can be calculated easily. However, other types of distributions can be used, such as a Kumaraswamy distribution, or any other type of distribution that would be apparent to a person of ordinary skill in the art. Thus whenever the present text refers to a “Beta distribution,” it will be understood that it can be replaced by the other types of distributions.

Each Beta distribution is uniquely defined by two parameters, alpha (α) and beta (β). The alpha-beta parameters specify a unique shape of the Beta distribution, where the alpha and beta parameters jointly specify the mean (μ) and the standard deviation (σ) of the Beta distribution. The Beta distribution can also be applied to the posterior probability distribution discussed above and the likelihood probability distribution discussed below.

With respect to the technology disclosed, P(H) is the prior probability distribution of a conversion rate (also referred to as a prior conversion rate probability distribution) calculated for a particular candidate, which represents uncertainty about the conversion rate before the current data E is examined.

As discussed in more detail below with respect to FIG. 20, the technology disclosed implements a unique approach to determining the prior conversion rate probability distribution. Specifically, the technology disclosed obtains the posterior conversion rate probability distributions of parent candidates of the particular candidate (e.g., child candidate) being considered. In other words, for each parent candidate, the posterior probability distribution is obtained in dependence upon actual performance of the parent candidate, and then the prior probability distribution is calculated for the child candidate as a combination of the obtained posterior probability distributions of all of the parent candidates of the child candidate. This technique provides an accurate prior conversion rate probability distribution because conversion rate distributions of the parents are already known through testing and the child candidate being considered has evolved from both of the parents and will have similar characteristics as the parents. This technique can be referred to as the parent-based prior technique.

The parent-based prior technique can determine the prior conversion rate probability distribution of a child candidate (e.g., candidate C) by combining the posterior conversion rate probability distributions of the parent candidates (e.g., candidates A and B). Although this example describes only two parents, there can be more than two parents involved in the procreation of a candidate. The posterior conversion rate probability distributions of the parent candidates A and B can be combined to form or create the prior conversion rate probability distribution calculated for the child candidate C by, for example, setting the mean of the prior conversion rate probability distribution calculated for the child candidate C as an averaged mean of the posterior conversion rate probability distributions of parent candidates A and B. Using this technique, generally, in the pool of candidates, the mean of each child's prior conversion rate probability distribution will be different. As an alternative to using the means of the posterior conversion rate probability distributions of the parent candidates A and B to set the mean of the prior conversion rate probability distribution calculated for the child candidate C, the technology disclosed may (i) use the medians of the posterior conversion rate probability distributions of parent candidates A and B to set the median of the prior conversion rate probability distribution calculated for the child candidate C, (ii) use the modes of the posterior conversion rate probability distributions of parents candidates A and B to set the mode of the prior conversion rate probability distribution calculated for the child candidate C, and/or (iii) weight the means, medians and/or modes of one or more of the posterior conversion rate probability distributions of the parent candidates A and B based on their experience or any other criteria that is available in order to set the means, median and/or mode of the prior conversion rate probability distribution calculated for the child candidate C. Other criteria that would be apparent to a person of ordinary skill in the art can be used from the posterior conversion rate probability distributions of the parent candidates A and B to determine characteristics of the child's prior conversion rate probability distribution.

Additionally, the standard deviation of the prior conversion rate probability distribution of the child candidate C can be formulated from a maximum likelihood estimation problem. For example, a single standard deviation value for the prior conversion rate probability distribution can be calculated using the entire population of tested candidates or the entire population of active candidate individuals of the same or previous generations with respect to the generation of the parent candidates or the child candidate. As an alternative to using the entire population of active candidates to calculate the single standard deviation, the single standard deviation value can be calculated using the conversion rates of (i) the most recent generation of candidates, (ii) X most recent generations of candidates (X being an integer) or (iii) only candidates that are in the family tree of the generated child. Other alternatives would be apparent to a person of ordinary skill in the art. The single standard deviation can be calculated by observing the means, the observed number of conversions, and a total number of visitors. The observed mean/median/modal conversion rates of each candidate in the population of candidates provides a set of scalars, such that an intermediary Beta distribution function can be fit over this set of scalars. The mean and standard deviation can be determined from the intermediary Beta distribution function, where the mean and standard deviation are fixed. When determining the prior conversion rate probability distribution of the child candidate C, (i) the mean of the intermediary Beta distribution function is ignored because the average mean of the posterior probability rates of parent candidates is used, as discussed above and (ii) the standard deviation value obtained from the intermediary Beta distribution function is used as the standard deviation of the prior conversion rate probability distribution calculated for the child candidate C. For each new offspring candidate, the alpha and beta for its prior probability conversion rate distribution is calculated, such that the prior probability conversion rate distribution uses the mean from its parents and the standard deviation of the calculated for the entire candidate pool as discussed above.

Generally, P (E IH) is the Bayesian “likelihood probability distribution” of observing event E given that H has occurred. P (E|H) is oftentimes referred to as observed data. With respect to the technology disclosed, P (E|H) is the likelihood probability distribution of the particular candidate individual being a good solution to the provided problem, given that the solution exists in the candidate pool. With respect to conversion rates, P (E|H) is the likelihood conversion rate probability distribution, which is a probability distribution of the particular candidate individual converting the shopper to the buyer, given that the shopper has been previously converted to the buyer.

Generally, P(E) is referred to as the marginal likelihood or “model evidence” and is usually the same for all possible hypotheses being considered and can be used to normalize the result of the multiplication of the prior probability distribution and the likelihood probability distribution. With respect to the technology disclosed, P(E) is a normalization factor so that the posterior probability conversion rate distribution sums up to a value of 1. Further details regarding the normalization factor are described in more detail below.

FIG. 20 illustrates using Bayesian posterior probability distributions of parent candidates A and B to determine a Bayesian prior probability distribution of a child candidate C.

Referring the FIG. 20, the posterior probability distribution 2002 of parent A is combined with the posterior probability distribution 2004 of parent B to determine the prior probability distribution 2006 for child C. The prior probability distribution 2006 is combined with a likelihood probability distribution 2008 calculated for child candidate C to determine the posterior probability distribution 2010 of child candidate C.

The determined posterior probability distribution 2010 of child candidate C can then be used to determine whether or not child candidate C is the best solution to the problem compared to other candidates in the candidate pool.

For example, at any moment during evolution of candidates from the candidate pool, a Probability to Beat All (PTBA) metric (or score) can be calculated for each active candidate that makes up the current active population and can be calculated using the posterior probability distributions of all of the active candidates. The candidate with the highest PTBA metric (or score) will be the candidate individual of the candidate pool that is the most likely to successfully achieve a conversion of an end user from a shopper to a buyer. The PTBA score of a candidate individual is defined herein as the probability of the unknown true conversion rate to be the largest one in a selected population of candidates (e.g., all candidates, most recent generations of candidates, etc.). A candidate can be selected as a winning candidate individual from the pool of candidates, where the selected candidate has a highest/best probability to beat all of the other candidates. The score of each of the active candidates is calculated based on its relative performance to the other active candidates. The performance of the other active candidates can be referred to as a “predetermined score.” By definition, the sum of the PTBA scores in an active population of candidates is equal to 1. PTBA scores can be used for parent selection for creating new candidates and can be used for selecting a candidate or candidates as the best solution to the problem. Parents can be randomly selected with a probability proportional to their PTBA scores. The population size can be maintained so as to remain fixed across successive generations by discarding candidates with lower PTBA scores.

As previously mentioned, in some embodiments the winner selector 112 performs the “winner selection” as a post-processing step to evolution, such that it does not affect the evolution itself. Further, the “winner selection” does not require additional sampling beyond that done during evolution. This provides an advantage, because in practice, evolution may have to be terminated at any time based on customer request, and a good candidate needs to be delivered without further testing. In practice such termination often happens early, before evolution has fully converged, when candidate fitnesses still vary significantly (i.e., the estimates are uncertain and are unreliable and/or stochastic). This amplifies the need for reliable and accurate “winner selection.” Other implementations of selecting a winner can include selecting multiple winners at a given point to populate a new pool and/or to completely repopulate the candidate individual population 102.

Applying Bayesian Techniques to Root Candidates. If candidates to be analyzed from the candidate pool are root or first-generation candidates with no parents, the Bayesian techniques described above for calculating the prior conversion rate probability distribution will not be possible. This is because it will be impossible to gather any posterior conversion rate probability distributions from parent candidates when they do not exist. Therefore, for the root or first-generation parents, the a and values for the Beta distribution can be set using a different methodology. One methodology is to set the prior conversion rate probability distribution as a naive non-informative prior distribution with both α and β of the Beta distribution having a value of 1. Another methodology is to formulate this as a maximum likelihood estimation problem.

Credible Intervals. In contrast to Frequentist statistical methods that employ confidence intervals that treat the true conversion rate θ as being fixed, the Bayesian approach makes use of a credible interval that treats the conversion rate θ as a random variable. This is achieved by imposing a prior probability distribution on the true conversion rate θ using the Beta probability distribution. Once the posterior probability distribution has been constructed, which in embodiments herein can be summarized as determining α and β, a single value conversion rate estimate can be achieved. However, since θ is not fixed, it is beneficial to understand the extent of uncertainty we have in the single value conversion rate estimate. If, for example, a 95% credible interval is desired, the interval can be constructed such that P (a<θ<b)=0.95, where a and b represent the lower and upper bounds of the credible interval, respectively. This interval represents the subjective probability that θ∈(a, b).

Given the fact that the posterior probability distribution is calculated as a function of θ, β, k and n (i. e. , ƒ(θ; α+k, β+n−k)), where n represents number of visitors and k represents number of conversions, the credibility interval can be calculated using a percent point function, which is the inverse of the cumulative distribution function. For example, using Python®, a ppf method of a beta function in a SciPy module can be used to compute the credibility interval.

Accounting for Delayed Conversions. In many real-world cases, visitors (shoppers) do not instantly convert to buyers. They might visit the website several times, perhaps over the course of a few days, before they decide to convert. This is problematic for the comparison of candidates because they don't all have traffic of the same age. In other words, the conversion of some visitors will take place after the point of winner selection, parent selection and candidate discarding and will hence not be accounted for during decision making (e.g., winner selection, etc.).

In order to overcome this problem, the technology disclosed can account for the delayed conversion in the computation of prior (conversion rate) probability distribution, credible intervals, and a probability of beating control. Specifically, the technology disclosed can derive the probability of the visitors who will convert after the point of decision making by analyzing a histogram of the conversion age, which is the time it takes for a user to convert. A correction factor can then be calculated to apply to the posterior conversion rate probability distribution to adjust for the delayed conversion.

FIG. 21A illustrates Bayesian posterior (Beta) probability distributions of a candidate and also illustrates a probability (of a particular candidate) to beat all of the other candidates. Specifically, the upper plot of FIG. 21A provides a visual reference that shows the posterior Beta distribution of a candidate with varying mean, where the number of number of visitors equals 4,000. This plot also illustrates the posterior Beta distribution of a control candidate. Theta (θ) on the horizontal axis indicates the conversion rate and the vertical axis indicates probability density. The lower plot of FIG. 21A illustrates the probability to beat all of the same candidate over a standard control candidate that has a conversion rate of 5%, where theta (θ) on the horizontal axis indicates the conversion rate and the vertical axis indicates the PTBA with only a given candidate and a control candidate considered to make up the active candidate population.

FIG. 21B illustrates a function derived from a histogram of collected conversion age data that is used to account for delayed conversion rates when determining a Bayesian posterior conversion rate probability distribution of a candidate. This plot shows the distribution of the conversion age (in days) for each conversion event in an experiment. The solid gray bars show the histogram of the collected data regarding conversion age. The solid blue line shows the best fitting function over this histogram. This best fitting function is modeled to have 3 distinct phases with undetermined boundaries indicated in red circular marks. The 1^(st) phase (transient phase) has an e^(−t2) shape, the 2^(nd) phase (exponential decay) has an e^(−t) shape and the 3^(rd) phase (termination phase) has an e^(−t) ² shape. This best fitting function (the conversion age distribution model) is then used to calculate the correction factor, which subsequently is used to update the data regarding the total number of conversions that serves as in input parameter (alpha) to the calculated posterior conversion rate probability distribution of the candidate. The correction factor is calculated from the best fitting function via integrating the following conditional probability equation: P(converted=True|T=t)=(P(T=t|converted=True)*P(converted=True)/P(T=t)), where “T” is the session length random variable, regardless of whether it ended with conversion or not and “t” is the known session length of the user. At any given instance during the experiment, when a parent selection or culling decision is to be made, for each end user who has yet not converted, their probability of converting in the future will be estimated by integrating the above formula between the bounds of the current session length “t” and the maximum session length value (e.g., 30 days).

Computer System

FIG. 22 is a simplified block diagram of a computer system that can be used to implement the technology disclosed. Computer system 2210 typically includes at least one processor 2214 that communicates with a number of peripheral devices via bus subsystem 2212. These peripheral devices can include a storage subsystem 2224 including, for example, memory devices and a file storage subsystem, user interface input devices 2222, user interface output devices 2218, and a network interface subsystem 2216. The input and output devices allow user interaction with computer system 2210. Network interface subsystem 2216 provides an interface to outside networks, including an interface to corresponding interface devices in other computer systems.

In one implementation, the conversion system 104 is communicably linked to the storage subsystem 2224 and user interface input devices 2222.

User interface input devices 2222 or clients or client devices can include a keyboard; pointing devices such as a mouse, trackball, touchpad, or graphics tablet; a scanner; a touch screen incorporated into the display; audio input devices such as voice recognition systems and microphones; and other types of input devices. In general, use of the term “input device” is intended to include all possible types of devices and ways to input information into computer system 2210.

User interface output devices 2218 can include a display subsystem, a printer, a fax machine, or non-visual displays such as audio output devices. The display subsystem can include a cathode ray tube (CRT), a flat-panel device such as a liquid crystal display (LCD), a projection device, or some other mechanism for creating a visible image. The display subsystem can also provide a non-visual display such as audio output devices. In general, use of the term “output device” is intended to include all possible types of devices and ways to output information from computer system 2210 to the user or to another machine or computer system. Storage subsystem 2224 stores programming and data constructs that provide the functionality of some or all of the modules and methods described herein. These software modules are generally executed by processor 2214 alone or in combination with other processors.

Memory 2226 used in the storage subsystem can include a number of memories including a main random-access memory (RAM) 2234 for storage of instructions and data during program execution and a read only memory (ROM) 2232 in which fixed instructions are stored. A file storage subsystem 2228 can provide persistent storage for program and data files, and can include a hard disk drive, a floppy disk drive along with associated removable media, a CD-ROM drive, an optical drive, or removable media cartridges. The modules implementing the functionality of certain implementations can be stored by file storage subsystem 2228 in the storage subsystem 2224, or in other machines accessible by the processor.

Bus subsystem 2212 provides a mechanism for letting the various components and subsystems of computer system 2210 communicate with each other as intended. Although bus subsystem 2212 is shown schematically as a single bus, alternative implementations of the bus subsystem can use multiple busses. Application server 2220 can be a framework that allows the applications of computer system 2210 to run, such as the hardware and/or software, e.g., the operating system.

Computer system 2210 itself can be of varying types including a personal computer, a portable computer, a workstation, a computer terminal, a network computer, a television, a mainframe, a server farm, a widely-distributed set of loosely networked computers, or any other data processing system or user device. Due to the ever-changing nature of computers and networks, the description of computer system 2210 depicted in FIG. 22 is intended only as a specific example for purposes of illustrating the preferred embodiments of the present invention. Many other configurations of computer system 2210 are possible having more or less components than the computer system depicted in FIG. 20.

The preceding description is presented to enable the making and use of the technology disclosed. Various modifications to the disclosed implementations will be apparent, and the general principles defined herein may be applied to other implementations and applications without departing from the spirit and scope of the technology disclosed.

For example, other ways can be used to calculate the priors based on having testing information on previously tested candidates. In some embodiments, the prior can be calculated from every candidate that has been tested so far and use it for all future candidates. As another example, performance can be predicted with a Gaussian distribution of performance of similar candidates—with only a few variants changed. As yet another example, aspects of the techniques described herein can be applied to numerous targets of evolution-based systems other than MLCO. Many other variations will be apparent to the reader. As yet another example, the family-tree based Bayesian method to estimate performance also can be used for candidate ranking for active use during the evolutionary process and can also be used to report more informed metrics on the progress of the evolution.

A given event or value is “responsive” (e.g., “in response to” or “responsive to”) to a predecessor event or value if the predecessor event or value influenced the given event or value. If there is an intervening processing element, step or time period, the given event or value can still be “responsive” to the predecessor event or value. If the intervening processing element or step combines more than one event or value, the signal output of the processing element or step is considered “responsive” to each of the event or value inputs. If the given event or value is the same as the predecessor event or value, this is merely a degenerate case in which the given event or value is still considered to be “responsive” to the predecessor event or value. “Dependency” (e.g. “in dependence upon” or “in dependence on”) of a given event or value upon another event or value is defined similarly.

The technology disclosed is not intended to be limited to the implementations shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein. The scope of the technology disclosed is defined by the appended claims. 

What is claimed is:
 1. A computer-implemented method for finding a best solution to a provided problem by selecting a winning candidate individual, the computer-implemented method comprising: storing, in a memory, a population of candidate individuals in a candidate pool; a computer system evolving the candidate individuals in the candidate pool by performing evolution steps including: testing each candidate individual of the candidate individuals to obtain test results, assigning a performance measure to each of the tested candidate individuals in dependence upon the test results, discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and adding, to the candidate pool, a new candidate individual procreated from parent candidate individuals remaining in the candidate pool after the discarding of the candidate individuals; repeating the evolution steps to evolve the candidate individuals in the candidate pool; and a computer system selecting, as a winning candidate individual, a candidate individual from the candidate pool having a best probability to beat a predetermined score, the probability to beat the predetermined score being determined in dependence upon a Bayesian posterior probability distribution of a particular candidate individual.
 2. The computer-implemented method of claim 1, wherein the posterior probability distribution of a particular candidate individual is determined in dependence upon (i) performance measures of parent candidate individuals of the particular candidate and (ii) a performance measure of the particular candidate.
 3. The computer-implemented method of claim 1, wherein the posterior probability distribution of a particular candidate individual predicts a future performance of the particular candidate individual and is calculated in dependence upon a Bayesian prior probability distribution for the particular candidate individual and a Bayesian likelihood probability distribution for the particular candidate individual.
 4. The computer-implemented method of claim 3, wherein the posterior probability distribution of each particular candidate individual of the candidate pool is calculated by: for each parent candidate individual of the particular candidate individual, obtaining a posterior probability distribution in dependence upon actual performance of the parent candidate individual; calculating the prior probability distribution for the particular candidate individual as a combination of the obtained posterior probability distributions of all parents of the particular candidate individual; calculating the posterior probability distribution of the particular candidate individual in dependence upon (i) the calculated prior probability distribution and (ii) the likelihood probability distribution, which is a probability distribution of observing data associated with the particular candidate individual; and calculating, in dependence upon the posterior probability distribution of the particular candidate individual, a probability of the particular candidate individual beating the predetermined score.
 5. The computer-implemented method of claim 4, wherein the posterior probability distribution of a particular candidate individual is proportional to the likelihood probability distribution for the particular candidate individual multiplied by the prior probability distribution for the particular candidate individual.
 6. The computer-implemented method of claim 4, wherein the prior probability distribution of each particular candidate individual has a mean and a standard deviation, and wherein the mean of the prior probability distribution for the particular candidate individual is calculated by averaging the means of the posterior probability distributions of all of the parent candidate individuals.
 7. The computer-implemented method of claim 4, wherein the prior probability distribution of each particular candidate individual has a mean and a standard deviation, and wherein the standard deviation of the prior probability distribution for the particular candidate individual is calculated using a maximum likelihood estimation to provide a single standard deviation to all candidate individuals in the candidate pool.
 8. The computer-implemented method of claim 4, wherein the prior probability distribution of each particular candidate individual has a mean and a standard deviation, wherein the mean of the prior probability distribution for the particular candidate individual is calculated by averaging the means of the posterior probability distributions of all of the parent candidate individuals, and wherein the standard deviation of the prior probability distribution for the particular candidate individual is calculated using a maximum likelihood estimation to provide a single standard deviation to all candidate individuals in the candidate pool.
 9. The computer-implemented method of claim 1, further comprising calculating the predetermined score in dependence upon one or more posterior probability distributions of candidate individuals of the candidate pool.
 10. The computer-implemented method of claim 1, wherein each candidate individual identifies a particular solution to the provided problem and identifies a value for each of a plurality of dimensions of the particular solution.
 11. The computer-implemented method of claim 10, wherein each candidate individual represents a webpage configuration and the identified values for the plurality of dimensions represent different characteristics of the webpage configuration.
 12. The computer-implemented method of claim 10, wherein one characteristic of the different characteristics of the webpage configuration is a color of a portion of the webpage.
 13. The computer-implemented method of claim 10, wherein the selected winning candidate individual is the candidate individual determined to be the most likely, of all candidate individuals in the candidate pool, to successfully achieve conversion of end users.
 14. The computer-implemented method of claim 1, wherein: each candidate individual represents a webpage configuration having a plurality of dimensions representing different characteristics of the webpage configuration, each dimension having a value; and the webpage configuration represented by the selected winning candidate individual is the configuration, of all the configurations represented by the candidate individuals in the candidate pool, most likely to successfully achieve a conversion of a shopper to a buyer.
 15. The computer-implemented method of claim 14, wherein a probability that a webpage configuration represented by a particular candidate individual will achieve the conversion of the shopper to the buyer is calculated in dependence upon a Bayesian posterior conversion rate probability distribution of the particular candidate individual that is determined in dependence upon a Bayesian prior conversion rate probability distribution for the particular candidate individual and a Bayesian likelihood conversion rate probability distribution for the particular candidate individual.
 16. The computer-implemented method of claim 15, wherein the posterior conversion rate probability distribution of each particular candidate individual is calculated by: for each parent candidate individual of the particular candidate individual, obtaining a posterior conversion rate probability distribution in dependence upon actual conversion rate performance of the parent candidate individual; calculating the prior conversion rate probability distribution for the particular candidate individual as a combination of the obtained posterior conversion rate probability distributions of all parents of the particular candidate individuals calculating the posterior conversion rate probability distribution of the particular candidate individual in dependence upon (i) the calculated prior conversion rate probability distribution and (ii) the likelihood conversion rate probability distribution, which is a probability distribution of observing collected data related to a number of shoppers and a number of conversions associated with the particular candidate individual; and calculating, in dependence upon the posterior conversion rate probability distribution of the particular candidate individual, a probability of the particular candidate individual beating the predetermined score.
 17. The computer-implemented method of claim 16, wherein the posterior conversion rate probability distribution of a particular candidate individual is proportional to the likelihood conversion rate probability distribution for the particular candidate individual multiplied by the prior conversion rate probability distribution for the particular candidate individual.
 18. The computer-implemented method of claim 16, wherein the prior conversion rate probability distribution of each particular candidate individual has a mean and a standard deviation, and wherein the mean of the prior conversion rate probability distribution for the particular candidate individual is calculated by averaging the means of the posterior conversion rate probability distributions of all of the parent candidate individuals.
 19. The computer-implemented method of claim 16, wherein the prior conversion rate probability distribution of each particular candidate individual has a mean and a standard deviation, and wherein the standard deviation of the prior conversion rate probability distribution for the particular candidate individual is calculated using a maximum likelihood estimation to provide a single standard deviation to all candidate individuals in the candidate pool.
 20. The computer-implemented method of claim 16, wherein the prior conversion rate probability distribution of each particular candidate individual has a mean and a standard deviation, wherein the mean of the prior conversion rate probability distribution for the particular candidate individual is calculated by averaging the means of the posterior conversion rate probability distributions of all of the parent candidate individuals, and wherein the standard deviation of the prior conversion rate probability distribution for the particular candidate individual is calculated using a maximum likelihood estimation to provide a single standard deviation to all candidate individuals in the candidate pool.
 21. The computer-implemented method of claim 1, wherein the performance measures assigned to each of the tested candidate individuals during the performing of the evolution steps is determined in dependence upon Bayesian posterior probability distributions of the tested candidate individuals.
 22. The computer-implemented method of claim 21, wherein the posterior probability distribution of each tested candidate individual predicts a future performance of the tested candidate individual and is calculated in dependence upon a Bayesian prior probability distribution for the tested candidate individual and a Bayesian likelihood probability distribution for the tested candidate individual.
 23. A non-transitory computer-readable recording medium having instructions recorded thereon for finding a best solution to a provided problem by selecting a winning candidate individual, the instructions, when executed by a processor of a computer, causing the computer to execute a method comprising: storing, in a memory, a population of candidate individuals in a candidate pool; evolving the candidate individuals in the candidate pool by performing evolution steps including: testing each candidate individual of the candidate individuals to obtain test results, assigning a performance measure to each of the tested candidate individuals in dependence upon the test results, discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and adding, to the candidate pool, a new candidate individual procreated from parent candidate individuals remaining in the candidate pool after the discarding of the candidate individuals; repeating the evolution steps to evolve the candidate individuals in the candidate pool; and selecting, as a winning candidate individual, a candidate individual from the candidate pool having a best probability to beat a predetermined score, the probability to beat the predetermined score being determined in dependence upon a Bayesian posterior probability distribution of a particular candidate individual.
 24. A computer-implemented system for finding a best solution to a provided problem by selecting a winning candidate individual, the computer-implemented system comprising: a memory storing a population of candidate individuals in a candidate pool; a conversion system: evolving the candidate individuals in the candidate pool by performing evolution steps including: testing each candidate individual of the candidate individuals to obtain test results, assigning a performance measure to each of the tested candidate individuals in dependence upon the test results, discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and adding, to the candidate pool, a new candidate individual procreated from parent candidate individuals remaining in the candidate pool after the discarding of the candidate individuals, and repeating the evolution steps to evolve the candidate individuals in the candidate pool; and a winner selector selecting, as a winning candidate individual, a candidate individual from the candidate pool having a best probability to beat a predetermined score, the probability to beat the predetermined score being determined in dependence upon a Bayesian posterior probability distribution of a particular candidate individual. 